وظائف Infomineo

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>About us Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence.<br> We’ve evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise.<br> We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling.<br> This is made possible by relying on 3 pillars of excellence: 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona).<br> Our proprietary AI orchestrator.<br> Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions.<br> Ready to kick start your career with us?<br> About this role This role will give you the opportunity to work on high added value data governance and data quality projects, building robust data management frameworks for our clients within a growing service company.<br> As our Data Governance Specialist, you will help clients take control of their data assets — defining policies, enforcing quality standards, managing metadata, and deploying governance tooling.<br> You will collaborate closely with data engineers, analysts, and client stakeholders to embed governance practices across the full data lifecycle.<br> What will you do?<br> Design, implement, and operationalize data governance frameworks across client engagements, covering the following areas: DATA GOVERNANCE &amp; POLICY Define and implement data governance frameworks, policies, and standards tailored to client environments.<br> Establish data ownership, stewardship models, and accountability structures across business and technical teams.<br> Develop and maintain data dictionaries, business glossaries, and classification taxonomies.<br> Ensure compliance with data regulations and internal data management policies (GDPR, BCBS 239, etc.<br>). DATA QUALITY &amp; LINEAGE Design and implement data quality rules, profiling routines, and monitoring dashboards.<br> Investigate and remediate data quality issues across structured and unstructured data sources.<br> Map and document end-to-end data lineage to support auditability and impact analysis.<br> Define and track data quality KPIs and SLAs in collaboration with data owners.<br> METADATA &amp; CATALOGUING Deploy and administer data catalog and metadata management tools (e.<br>g. Informatica, Collibra, Alation, Microsoft Purview).<br> Enrich metadata assets with business context, ownership, sensitivity classification, and usage information.<br> Drive adoption of the data catalog across business and technical teams.<br> TOOLING &amp; INTEGRATION Configure and operate data governance platforms, primarily Informatica (IDMC, Axon, EDC, DQ) and equivalent tools.<br> Integrate governance tooling with existing data pipelines, warehouses, and BI environments.<br> Automate data quality checks and governance workflows within ETL/ELT pipelines.<br> OTHER Provide internal training and knowledge-sharing sessions on data governance best practices.<br> Support the Team Lead/Manager on client relationships, business development, and internal projects.<br> Who are you?<br> EDUCATION &amp; PROFESSIONAL EXPERIENCE Master’s degree in a relevant field such as Computer Science, Information Systems, Data Management, Statistics, or Applied Mathematics.<br> Full proficiency in French and English 3 to 6 years of experience in data governance, data management, or data engineering with a strong governance focus.<br> Experience working in a consulting, financial services, or multi-client environment is a plus.<br> TECHNICAL SKILLS DATA GOVERNANCE TOOLS Proven hands-on experience with Informatica (IDMC / Intelligent Data Management Cloud, Axon Data Governance, Enterprise Data Catalog, Data Quality).<br> Experience with other governance and cataloguing platforms such as Collibra, Alation, Ataccama, or Microsoft Purview.<br> Familiarity with data quality tools: Great Expectations, Monte Carlo, Soda, or equivalent.<br> DATA ENGINEERING &amp; QUERYING Solid experience with SQL for data profiling, querying, and quality rule implementation across relational databases and data warehouses (Snowflake, BigQuery, Synapse, Redshift).<br> Experience in Python for automating data quality checks, metadata extraction, and governance workflows.<br> Understanding of ETL/ELT pipelines and ability to embed governance controls within them (dbt, Airflow, Informatica PowerCenter/IDMC).<br> DATA ARCHITECTURE &amp; PLATFORMS Good understanding of data platform architectures: data lake, lakehouse, and data warehouse environments.<br> Familiarity with cloud platforms and their native governance services: Azure Purview, AWS Glue Data Catalog, Google Dataplex.<br> Understanding of data modelling concepts and their impact on governance (dimensional modelling, data vault, etc.<br>). STANDARDS &amp; COMPLIANCE Knowledge of data governance frameworks and standards: DAMA-DMBOK, BCBS 239, GDPR, ISO 8000.<br> Experience defining and applying data classification schemes (sensitivity labels, PII tagging, retention policies).<br> Exposure to master data management (MDM) concepts and tools is a plus.<br> INTERPERSONAL SKILLS Ability to engage and influence both technical teams and business stakeholders on data governance topics.<br> Strong analytical mindset with the ability to diagnose data quality issues and translate them into actionable remediation plans.<br> Good communication skills with the ability to document governance frameworks clearly and present findings to senior audiences.<br> Detail-oriented, rigorous, and comfortable working across multiple client contexts simultaneously.<br> What we offer A competitive salary.<br> A great working environment.<br> A steep learning curve with interesting and diverse topics to work on.<br> A healthy work-life balance.<br> Health insurance benefits.<br> Equal opportunity employer Infomineo is an equal opportunity employer, we prohibit any sort of discrimination (based on color, race, sex, sexual orientation, religion, national origin or any other attributes) in all aspects of employment (recruiting, hiring, wages and salary, promotions, benefits, training and job termination).<br> If you believe you match our requirements and values, we would be happy to hear from you.<br> Visit our website to know more about us, our services and company culture.<br></span> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p><b>&nbsp;About this role</b> This role will give you the opportunity to work on high added value data governance and data quality projects, building robust data management frameworks for our clients within a growing service company. As our Data Governance Specialist, you will help clients take control of their data assets defining policies, enforcing quality standards, managing metadata, and deploying governance tooling. You will collaborate closely with data engineers, analysts, and client stakeholders to embed governance practices across the full data lifecycle. What will you do? Design, implement, and operationalize data governance frameworks across client engagements, covering the following areas: DATA GOVERNANCE &amp; POLICY Define and implement data governance frameworks, policies, and standards tailored to client environments. Establish data ownership, stewardship models, and accountability structures across business and technical teams. Develop and maintain data dictionaries, business glossaries, and classification taxonomies. Ensure compliance with data regulations and internal data management policies (GDPR, BCBS 239, etc.). DATA QUALITY &amp; LINEAGE Design and implement data quality rules, profiling routines, and monitoring dashboards. Investigate and remediate data quality issues across structured and unstructured data sources. Map and document end-to-end data lineage to support auditability and impact analysis. Define and track data quality KPIs and SLAs in collaboration with data owners. METADATA &amp; CATALOGUING Deploy and administer data catalog and metadata management tools (e.g. Informatica, Collibra, Alation, Microsoft Purview). Enrich metadata assets with business context, ownership, sensitivity classification, and usage information. Drive adoption of the data catalog across business and technical teams. TOOLING &amp; INTEGRATION Configure and operate data governance platforms, primarily Informatica (IDMC, Axon, EDC, DQ) and equivalent tools. Integrate governance tooling with existing data pipelines, warehouses, and BI environments. Automate data quality checks and governance workflows within ETL/ELT pipelines. OTHER Provide internal training and knowledge-sharing sessions on data governance best practices. Support the Team Lead/Manager on client relationships, business development, and internal projects. Who are you? EDUCATION &amp; PROFESSIONAL EXPERIENCE Master s degree in a relevant field such as Computer Science, Information Systems, Data Management, Statistics, or Applied Mathematics. Full proficiency in French and English 3 to 6 years of experience in data governance, data management, or data engineering with a strong governance focus. Experience working in a consulting, financial services, or multi-client environment is a plus. TECHNICAL SKILLS DATA GOVERNANCE TOOLS Proven hands-on experience with Informatica (IDMC / Intelligent Data Management Cloud, Axon Data Governance, Enterprise Data Catalog, Data Quality). Experience with other governance and cataloguing platforms such as Collibra, Alation, Ataccama, or Microsoft Purview. Familiarity with data quality tools: Great Expectations, Monte Carlo, Soda, or equivalent. DATA ENGINEERING &amp; QUERYING Solid experience with SQL for data profiling, querying, and quality rule implementation across relational databases and data warehouses (Snowflake, BigQuery, Synapse, Redshift). Experience in Python for automating data quality checks, metadata extraction, and governance workflows. Understanding of ETL/ELT pipelines and ability to embed governance controls within them (dbt, Airflow, Informatica PowerCenter/IDMC). DATA ARCHITECTURE &amp; PLATFORMS Good understanding of data platform architectures: data lake, lakehouse, and data warehouse environments. Familiarity with cloud platforms and their native governance services: Azure Purview, AWS Glue Data Catalog, Google Dataplex. Understanding of data modelling concepts and their impact on governance (dimensional modelling, data vault, etc.). STANDARDS &amp; COMPLIANCE Knowledge of data governance frameworks and standards: DAMA-DMBOK, BCBS 239, GDPR, ISO 8000. Experience defining and applying data classification schemes (sensitivity labels, PII tagging, retention policies). Exposure to master data management (MDM) concepts and tools is a plus. INTERPERSONAL SKILLS Ability to engage and influence both technical teams and business stakeholders on data governance topics. Strong analytical mindset with the ability to diagnose data quality issues and translate them into actionable remediation plans.&nbsp;</p></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><h2>Who are you?</h2><p>EDUCATION &amp; PROFESSIONAL EXPERIENCE</p><ul><li>Master s degree in a relevant field such as Computer Science, Information Systems, Data Management, Statistics, or Applied Mathematics.</li><li>Full proficiency in French and English</li><li>3 to 6 years of experience in data governance, data management, or data engineering with a strong governance focus.</li><li>Experience working in a consulting, financial services, or multi-client environment is a plus.</li></ul><p>TECHNICAL SKILLS</p><p>DATA GOVERNANCE TOOLS</p><ul><li>Proven hands-on experience with Informatica (IDMC / Intelligent Data Management Cloud, Axon Data Governance, Enterprise Data Catalog, Data Quality).</li><li>Experience with other governance and cataloguing platforms such as Collibra, Alation, Ataccama, or Microsoft Purview.</li><li>Familiarity with data quality tools: Great Expectations, Monte Carlo, Soda, or equivalent.</li></ul><p>DATA ENGINEERING &amp; QUERYING</p><ul><li>Solid experience with SQL for data profiling, querying, and quality rule implementation across relational databases and data warehouses (Snowflake, BigQuery, Synapse, Redshift).</li><li>Experience in Python for automating data quality checks, metadata extraction, and governance workflows.</li><li>Understanding of ETL/ELT pipelines and ability to embed governance controls within them (dbt, Airflow, Informatica PowerCenter/IDMC).</li></ul><p>DATA ARCHITECTURE &amp; PLATFORMS</p><ul><li>Good understanding of data platform architectures: data lake, lakehouse, and data warehouse environments.</li><li>Familiarity with cloud platforms and their native governance services: Azure Purview, AWS Glue Data Catalog, Google Dataplex.</li><li>Understanding of data modelling concepts and their impact on governance (dimensional modelling, data vault, etc.).</li></ul><p>STANDARDS &amp; COMPLIANCE</p><ul><li>Knowledge of data governance frameworks and standards: DAMA-DMBOK, BCBS 239, GDPR, ISO 8000.</li><li>Experience defining and applying data classification schemes (sensitivity labels, PII tagging, retention policies).</li><li>Exposure to master data management (MDM) concepts and tools is a plus.</li></ul><p>INTERPERSONAL SKILLS</p><ul><li>Ability to engage and influence both technical teams and business stakeholders on data governance topics.</li><li>Strong analytical mindset with the ability to diagnose data quality issues and translate them into actionable remediation plans.</li><li>Good communication skills with the ability to document governance frameworks clearly and present findings to senior audiences.</li><li>Detail-oriented, rigorous, and comfortable working across multiple client contexts simultaneously.</li></ul><p></p></section>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment: ARCHITECTURE &amp; DESIGN Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines. Define standards for data modeling, storage, ingestion, and transformation across client engagements. Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring). Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling. TEAM ENABLEMENT Define best practices and reusable frameworks for data engineers, analysts, and data scientists. Act as a technical mentor and reviewer for cross-functional project teams. Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects. Contribute to internal knowledge base, toolkits, and delivery accelerators. CLIENT ENGAGEMENT Lead architecture workshops and discovery sessions with client stakeholders. Translate business requirements into scalable, robust technical blueprints. Present architecture decisions to both technical teams and executive audiences. Support pre-sales and proposal efforts with technical scoping and solution design. OTHER Provide internal training and knowledge-sharing sessions with the team. Support the Head of Practice on business development and internal capability initiatives.</p></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>Master's degree in Computer Science, Data Engineering, Software Engineering, Applied Mathematics, or a related field. Full proficiency in English + 1 additional language (French, Arabic, Spanish, German...). 6+ years of technical experience in data architecture or a closely related field. Proven track record in a consulting or multi-client services environment. TECHNICAL SKILLS DATA ARCHITECTURE &amp; PLATFORMS Proven hands-on experience designing large-scale data platforms: data lake, lakehouse, or warehouse architectures (Databricks, Snowflake, BigQuery, Azure Synapse, Redshift). Strong command of SQL and at least one of Python, Scala, or Spark for data processing and transformation. Experience with Big Data ecosystems: Hadoop, Spark, PySpark, Hive, or equivalent. Familiarity with streaming and real-time architectures (Kafka, Flink, Spark Streaming). AI &amp; ML INFRASTRUCTURE Proven hands-on experience with ML lifecycle tooling: MLflow, Kubeflow, SageMaker, Azure ML, or equivalent. Experience architecting MLOps pipelines: model versioning, CI/CD for ML, monitoring and drift detection. Exposure to GenAI and LLM integration patterns (RAG architectures, vector databases, prompt pipelines). DATA ENGINEERING &amp; DEPLOYMENT Proven hands-on experience with orchestration and transformation tools: Airflow, dbt, or equivalent. Proven hands-on experience with container technologies: Docker, Kubernetes. Proven hands-on experience with versioning software: Git, GitHub, GitLab. Proven hands-on experience deploying solutions in cloud ecosystems: AWS, Azure, or Google Cloud. GOVERNANCE &amp; STANDARDS Knowledge of data governance frameworks: data catalogs, lineage tracking, access control, and data quality management. Exposure to BI and data visualization platforms (Power BI, Tableau, Looker) and semantic layer design. INTERPERSONAL SKILLS Ability to step back, analyze complex problems, define architectural options, and drive decisions. Strong ability to work and collaborate with a variety of stakeholders across technical and business functions. Excellent communication skills with the ability to translate complex technical architectures into clear business implications. High autonomy, attention to detail, and ability to manage multiple client engagements simultaneously.</p><p></p></section>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>About us Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence.<br> We’ve evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise.<br> We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling.<br> This is made possible by relying on 3 pillars of excellence: 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona).<br> Our proprietary AI orchestrator.<br> Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions.<br> Ready to kick start your career with us?<br> About this role This role will give you the opportunity to design and shape the technical foundation of our Analytics &amp; AI practice, working on high-impact client engagements across industries.<br> As our Analytics &amp; AI Architect, you will sit at the intersection of data engineering, analytics, data science, and AI — defining the standards, frameworks, and architectures that our teams build upon.<br> What will you do?<br> Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment: ARCHITECTURE &amp; DESIGN Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines.<br> Define standards for data modeling, storage, ingestion, and transformation across client engagements.<br> Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring).<br> Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling.<br> TEAM ENABLEMENT Define best practices and reusable frameworks for data engineers, analysts, and data scientists.<br> Act as a technical mentor and reviewer for cross-functional project teams.<br> Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects.<br> Contribute to internal knowledge base, toolkits, and delivery accelerators.<br> CLIENT ENGAGEMENT Lead architecture workshops and discovery sessions with client stakeholders.<br> Translate business requirements into scalable, robust technical blueprints.<br> Present architecture decisions to both technical teams and executive audiences.<br> Support pre-sales and proposal efforts with technical scoping and solution design.<br> OTHER Provide internal training and knowledge-sharing sessions with the team.<br> Support the Head of Practice on business development and internal capability initiatives.<br> Who are you?<br> EDUCATION &amp; PROFESSIONAL EXPERIENCE Master’s degree in Computer Science, Data Engineering, Software Engineering, Applied Mathematics, or a related field.<br> Full proficiency in English + 1 additional language (French, Arabic, Spanish, German.<br>..). 6+ years of technical experience in data architecture or a closely related field.<br> Proven track record in a consulting or multi-client services environment.<br> TECHNICAL SKILLS DATA ARCHITECTURE &amp; PLATFORMS Proven hands-on experience designing large-scale data platforms: data lake, lakehouse, or warehouse architectures (Databricks, Snowflake, BigQuery, Azure Synapse, Redshift).<br> Strong command of SQL and at least one of Python, Scala, or Spark for data processing and transformation.<br> Experience with Big Data ecosystems: Hadoop, Spark, PySpark, Hive, or equivalent.<br> Familiarity with streaming and real-time architectures (Kafka, Flink, Spark Streaming).<br> AI &amp; ML INFRASTRUCTURE Proven hands-on experience with ML lifecycle tooling: MLflow, Kubeflow, SageMaker, Azure ML, or equivalent.<br> Experience architecting MLOps pipelines: model versioning, CI/CD for ML, monitoring and drift detection.<br> Exposure to GenAI and LLM integration patterns (RAG architectures, vector databases, prompt pipelines).<br> DATA ENGINEERING &amp; DEPLOYMENT Proven hands-on experience with orchestration and transformation tools: Airflow, dbt, or equivalent.<br> Proven hands-on experience with container technologies: Docker, Kubernetes.<br> Proven hands-on experience with versioning software: Git, GitHub, GitLab.<br> Proven hands-on experience deploying solutions in cloud ecosystems: AWS, Azure, or Google Cloud.<br> GOVERNANCE &amp; STANDARDS Knowledge of data governance frameworks: data catalogs, lineage tracking, access control, and data quality management.<br> Exposure to BI and data visualization platforms (Power BI, Tableau, Looker) and semantic layer design.<br> INTERPERSONAL SKILLS Ability to step back, analyze complex problems, define architectural options, and drive decisions.<br> Strong ability to work and collaborate with a variety of stakeholders across technical and business functions.<br> Excellent communication skills with the ability to translate complex technical architectures into clear business implications.<br> High autonomy, attention to detail, and ability to manage multiple client engagements simultaneously.<br> What we offer A competitive salary.<br> A great working environment.<br> A steep learning curve with interesting and diverse topics to work on.<br> A healthy work-life balance.<br> Health insurance benefits.<br> Equal opportunity employer Infomineo is an equal opportunity employer, we prohibit any sort of discrimination (based on color, race, sex, sexual orientation, religion, national origin or any other attributes) in all aspects of employment (recruiting, hiring, wages and salary, promotions, benefits, training and job termination).<br> If you believe you match our requirements and values, we would be happy to hear from you.<br> Visit our website to know more about us, our services and company culture.<br></span> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>Translate business and client requirements into data science approaches, AI workflows, and scalable technical solutions. Design, prototype, and productionize machine learning, LLM, and generative AI solutions with a focus on business value, reliability, and usability. Own the architecture of Retrieval-Augmented Generation (RAG) pipelines, including document processing, vectorization, semantic search, evaluation, and query optimization for enterprise use cases. Design and implement complex AI-powered features by integrating LLM APIs and services using frameworks such as LangChain or equivalent, with a focus on reliability, accuracy, and performance in production. Design, implement, and maintain Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and data sources, enabling context-aware and extensible AI solutions. Develop evaluation frameworks, monitoring approaches, and observability practices for LLM-powered systems to ensure quality, transparency, and continuous improvement. Apply advanced prompt engineering, embedding strategies, and vector database management techniques to improve the performance of AI solutions. Integrate AI outputs into analytical workflows, dashboards, reporting tools, and client delivery pipelines. Full-Stack Application Development: Design and contribute to the development of production-grade AI and data applications, primarily using Python and backend frameworks such as FastAPI. Build or support frontend interfaces using modern frameworks such as React, Next.js, or Vue to make AI and data products accessible to business users and clients. Collaborate with software engineers to define scalable application architectures, API standards, and integration patterns. Develop and maintain REST API integrations with third-party AI services, enterprise SaaS platforms, internal tools, and external data sources. Ensure that data science prototypes are translated into maintainable, secure, and scalable production solutions. Participate in code reviews, define technical best practices, and contribute to a high-quality engineering and data science culture. Cloud, Deployment &amp; MLOps: Support the containerization and cloud deployment of AI and data applications, preferably on Google Cloud Platform using GKE and Artifact Registry, while remaining adaptable to other cloud environments. Design and maintain CI/CD pipelines using GitHub Actions or equivalent tools to ensure reliable and repeatable releases. Apply MLOps and LLMOps practices to manage experimentation, deployment, monitoring, and continuous improvement of AI systems. Collaborate with engineering and infrastructure teams to ensure the reliability, scalability, and performance of production environments. Proactively identify performance bottlenecks in AI workflows, data pipelines, application layers, and infrastructure. Technical Leadership &amp; Collaboration: Lead applied AI and data science initiatives from discovery and prototyping through production deployment. Mentor junior data scientists, AI engineers, and developers on data science methods, AI integration, coding practices, and production readiness. Work closely with product teams, consultants, analysts, and non-technical stakeholders to ensure solutions are aligned with business needs. Define standards and best practices for AI solution design, evaluation, documentation, and delivery. Communicate complex technical concepts clearly to both technical and non-technical audiences. Qualifications: 4 to 6 years of experience in data science, AI development, applied machine learning, or related technical roles, with hands-on experience delivering production-grade AI or data products. Strong proficiency in Python, with experience using data science, machine learning, and AI libraries and frameworks. Solid full-stack development background, including experience with backend frameworks such as FastAPI and modern frontend frameworks such as React, Next.js, or Vue. Deep understanding of LLMs, RAG architectures, generative AI workflows, and production-grade AI service integration, including tools such as OpenAI, Gemini, LangChain, or equivalent. Proven experience designing and implementing Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and enterprise data sources. Experience building analytical workflows, dashboards, data pipelines, or AI-powered decision-support tools in a client delivery or enterprise context. Hands-on experience with Docker and cloud deployment on at least one major cloud platform such as GCP, AWS, Azure, or equivalent. Familiarity with container orchestration, artifact management, CI/CD pipelines, GitHub Actions, GitOps workflows, and branching strategies. Strong understanding of LLM observability, AI evaluation, performance monitoring, and production reliability practices. Demonstrated ability to lead technical initiatives, mentor junior team members, and collaborate effectively with product teams and business stakeholders. Bachelor s or Master s degree in Data Science, Computer Science, Software Engineering, Statistics, Applied Mathematics, or a related field. Preferred Skills: Experience with agentic AI frameworks such as LangGraph or similar orchestration tools for building multi-step AI workflows. Knowledge of advanced prompt engineering, vector database management, embedding model optimization, and AI evaluation techniques. Experience with MLOps or LLMOps practices, including experiment tracking, model monitoring, and AI quality evaluation. Experience with Infrastructure as Code tools such as Terraform or Pulumi. Experience designing data models, analytical pipelines, or BI/dashboard solutions. Relevant certifications such as Google Cloud Professional Data Engineer, Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Developer, or similar cloud and AI credentials. What we offer: A competitive compensation and benefits package. The opportunity to lead AI, data science, and technology initiatives with real global impact. A dynamic and supportive work environment that values leadership, innovation, and your contributions. Continuous learning and professional development opportunities to propel your career forward in AI, data science, and technology. Application Process: Candidates are invited to submit a resume, cover letter, and any relevant portfolio, GitHub links, or project examples showcasing their experience in data science, AI solution development, full-stack application development, and cloud deployment. Shortlisted candidates will undergo a technical assessment and interview to demonstrate their data science expertise, AI implementation skills, full-stack capabilities, and technical leadership potential. Infomineo: Where brilliant minds meet to shape the future of business.</p></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>4 to 6 years of experience in data science, AI development, applied machine learning, or related technical roles, with hands-on experience delivering production-grade AI or data products. Strong proficiency in Python, with experience using data science, machine learning, and AI libraries and frameworks. Solid full-stack development background, including experience with backend frameworks such as FastAPI and modern frontend frameworks such as React, Next.js, or Vue. Deep understanding of LLMs, RAG architectures, generative AI workflows, and production-grade AI service integration, including tools such as OpenAI, Gemini, LangChain, or equivalent. Proven experience designing and implementing Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and enterprise data sources. Experience building analytical workflows, dashboards, data pipelines, or AI-powered decision-support tools in a client delivery or enterprise context. Hands-on experience with Docker and cloud deployment on at least one major cloud platform such as GCP, AWS, Azure, or equivalent. Familiarity with container orchestration, artifact management, CI/CD pipelines, GitHub Actions, GitOps workflows, and branching strategies. Strong understanding of LLM observability, AI evaluation, performance monitoring, and production reliability practices. Demonstrated ability to lead technical initiatives, mentor junior team members, and collaborate effectively with product teams and business stakeholders. Bachelor s or Master s degree in Data Science, Computer Science, Software Engineering, Statistics, Applied Mathematics, or a related field.</p><p>Preferred Skills:</p><ul><li>Experience with agentic AI frameworks such as LangGraph or similar orchestration tools for building multi-step AI workflows.</li><li>Knowledge of advanced prompt engineering, vector database management, embedding model optimization, and AI evaluation techniques.</li><li>Experience with MLOps or LLMOps practices, including experiment tracking, model monitoring, and AI quality evaluation.</li><li>Experience with Infrastructure as Code tools such as Terraform or Pulumi.</li><li>Experience designing data models, analytical pipelines, or BI/dashboard solutions.</li><li>Relevant certifications such as Google Cloud Professional Data Engineer, Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Developer, or similar cloud and AI credentials.</li></ul><p></p></section>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>About us Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence.<br> We’ve evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise.<br> We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling.<br> This is made possible by relying on 3 pillars of excellence: 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona).<br> Our proprietary AI orchestrator.<br> Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions.<br> Ready to kick start your career with us?<br> Why Infomineo?<br> Here’s what sets us apart: Shape the Future of Business Insights: You will be at the forefront, leading the design and implementation of AI-driven solutions that automate tasks and drive efficiency across our entire service spectrum (Business Research, Content, Design, and Data Analytics).<br> Work with Global Leaders: Our clients are industry leaders — Fortune 500s, top consultancies, governments, and NGOs.<br> You will take ownership of delivering technical solutions that directly support their success.<br> Lead in AI &amp; Technology: We foster continuous learning and technical excellence.<br> You will stay ahead of the latest advancements in AI and software engineering, and actively drive innovation across the team.<br> Thrive in a Collaborative Culture: We value intellectual curiosity, leadership, and a can-do attitude.<br> You will be encouraged to mentor others, contribute strategic ideas, and make a lasting impact on the company’s growth.<br> About this role: We are seeking an experienced and technically strong individual to join us as Senior AI Software Engineer.<br> In this role, you will take ownership of designing, developing, and deploying AI-powered data products and analytical applications that serve our internal teams and global clients.<br> You will lead data science and AI initiatives, define best practices for applied AI and analytical solution development, and collaborate closely with engineering, product, and business stakeholders.<br> While the role is primarily data science and AI-focused, you will also leverage your full-stack development background to build production-ready applications, integrate AI capabilities, and support cloud-based deployment.<br> Key Responsibilities: Data Science, AI &amp; Applied R&amp;D: Lead the design and development of AI-powered analytical solutions, data products, and intelligent applications that solve complex business problems.<br> Translate business and client requirements into data science approaches, AI workflows, and scalable technical solutions.<br> Design, prototype, and productionize machine learning, LLM, and generative AI solutions with a focus on business value, reliability, and usability.<br> Own the architecture of Retrieval-Augmented Generation (RAG) pipelines, including document processing, vectorization, semantic search, evaluation, and query optimization for enterprise use cases.<br> Design and implement complex AI-powered features by integrating LLM APIs and services using frameworks such as LangChain or equivalent, with a focus on reliability, accuracy, and performance in production.<br> Design, implement, and maintain Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and data sources, enabling context-aware and extensible AI solutions.<br> Develop evaluation frameworks, monitoring approaches, and observability practices for LLM-powered systems to ensure quality, transparency, and continuous improvement.<br> Apply advanced prompt engineering, embedding strategies, and vector database management techniques to improve the performance of AI solutions.<br> Integrate AI outputs into analytical workflows, dashboards, reporting tools, and client delivery pipelines.<br> Full-Stack Application Development: Design and contribute to the development of production-grade AI and data applications, primarily using Python and backend frameworks such as FastAPI.<br> Build or support frontend interfaces using modern frameworks such as React, Next.<br>js, or Vue to make AI and data products accessible to business users and clients.<br> Collaborate with software engineers to define scalable application architectures, API standards, and integration patterns.<br> Develop and maintain REST API integrations with third-party AI services, enterprise SaaS platforms, internal tools, and external data sources.<br> Ensure that data science prototypes are translated into maintainable, secure, and scalable production solutions.<br> Participate in code reviews, define technical best practices, and contribute to a high-quality engineering and data science culture.<br> Cloud, Deployment &amp; MLOps: Support the containerization and cloud deployment of AI and data applications, preferably on Google Cloud Platform using GKE and Artifact Registry, while remaining adaptable to other cloud environments.<br> Design and maintain CI/CD pipelines using GitHub Actions or equivalent tools to ensure reliable and repeatable releases.<br> Apply MLOps and LLMOps practices to manage experimentation, deployment, monitoring, and continuous improvement of AI systems.<br> Collaborate with engineering and infrastructure teams to ensure the reliability, scalability, and performance of production environments.<br> Proactively identify performance bottlenecks in AI workflows, data pipelines, application layers, and infrastructure.<br> Technical Leadership &amp; Collaboration: Lead applied AI and data science initiatives from discovery and prototyping through production deployment.<br> Mentor junior data scientists, AI engineers, and developers on data science methods, AI integration, coding practices, and production readiness.<br> Work closely with product teams, consultants, analysts, and non-technical stakeholders to ensure solutions are aligned with business needs.<br> Define standards and best practices for AI solution design, evaluation, documentation, and delivery.<br> Communicate complex technical concepts clearly to both technical and non-technical audiences.<br> Qualifications: 4 to 6 years of experience in data science, AI development, applied machine learning, or related technical roles, with hands-on experience delivering production-grade AI or data products.<br> Strong proficiency in Python, with experience using data science, machine learning, and AI libraries and frameworks.<br> Solid full-stack development background, including experience with backend frameworks such as FastAPI and modern frontend frameworks such as React, Next.<br>js, or Vue.<br> Deep understanding of LLMs, RAG architectures, generative AI workflows, and production-grade AI service integration, including tools such as OpenAI, Gemini, LangChain, or equivalent.<br> Proven experience designing and implementing Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and enterprise data sources.<br> Experience building analytical workflows, dashboards, data pipelines, or AI-powered decision-support tools in a client delivery or enterprise context.<br> Hands-on experience with Docker and cloud deployment on at least one major cloud platform such as GCP, AWS, Azure, or equivalent.<br> Familiarity with container orchestration, artifact management, CI/CD pipelines, GitHub Actions, GitOps workflows, and branching strategies.<br> Strong understanding of LLM observability, AI evaluation, performance monitoring, and production reliability practices.<br> Demonstrated ability to lead technical initiatives, mentor junior team members, and collaborate effectively with product teams and business stakeholders.<br> Bachelor’s or Master’s degree in Data Science, Computer Science, Software Engineering, Statistics, Applied Mathematics, or a related field.<br> Preferred Skills: Experience with agentic AI frameworks such as LangGraph or similar orchestration tools for building multi-step AI workflows.<br> Knowledge of advanced prompt engineering, vector database management, embedding model optimization, and AI evaluation techniques.<br> Experience with MLOps or LLMOps practices, including experiment tracking, model monitoring, and AI quality evaluation.<br> Experience with Infrastructure as Code tools such as Terraform or Pulumi.<br> Experience designing data models, analytical pipelines, or BI/dashboard solutions.<br> Relevant certifications such as Google Cloud Professional Data Engineer, Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Developer, or similar cloud and AI credentials.<br> What we offer: A competitive compensation and benefits package.<br> The opportunity to lead AI, data science, and technology initiatives with real global impact.<br> A dynamic and supportive work environment that values leadership, innovation, and your contributions.<br> Continuous learning and professional development opportunities to propel your career forward in AI, data science, and technology.<br> Application Process: Candidates are invited to submit a resume, cover letter, and any relevant portfolio, GitHub links, or project examples showcasing their experience in data science, AI solution development, full-stack application development, and cloud deployment.<br> Shortlisted candidates will undergo a technical assessment and interview to demonstrate their data science expertise, AI implementation skills, full-stack capabilities, and technical leadership potential.<br> Infomineo: Where brilliant minds meet to shape the future of business.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>About us Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence.<br> We've evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise.<br> We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling.<br> This is made possible by relying on 3 pillars of excellence: 1) 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona), 2) Our proprietary AI orchestrator, 3) Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions.<br> Ready to kick start your career with us?<br> Become the AI &amp; Automation Rockstar at Infomineo!<br> Do you dream of using cutting-edge AI and automation to revolutionize how businesses operate?<br> Infomineo, a fast-growing business insights powerhouse, is seeking a Senior Research &amp; Development Engineer to join our ranks and spearhead the development of next-gen solutions for our global clientele.<br> Why Infomineo?<br> Here's what sets us apart: Shape the Future of Business Insights: You'll be at the forefront, designing and implementing AI-driven solutions that automate tasks and boost efficiency across our entire service spectrum (Business Research, Content, Design, and Data Analytics).<br> Work with the Big Players: Our clients are industry leaders - Fortune 500s, consultancies, governments, and NGOs - and you'll play a vital role in supporting their success.<br> Become an AI &amp; Automation Guru: We foster continuous learning.<br> Expect to stay on top of the latest advancements and actively propose and implement improvements.<br> Global Impact, Local Connections: Infomineo boasts a vibrant international team with offices across 5 continents.<br> Collaborate with diverse, brilliant minds and gain exposure to a variety of industries and regions.<br> Thrive in a Collaborative Culture: We value intellectual curiosity, creativity, and a "can-do" attitude.<br> You'll be encouraged to contribute your ideas and make a real difference in the company's growth.<br> Does this sound like your dream role?<br> Here's what you'll bring to the table: 3-5 years of experience in backend development and distributed systems.<br> Hands-on experiences in building scalable, high-throughput applications using robust architectural components such as messaging queues, caching layers, connection/resource pools, and event-driven asynchronous processing.<br> Proven track record of contributing to the design and implementation of state-of-the-art software architectures, with a focus on clean code, design patterns, and maintainability for large-scale enterprise environments.<br> Deep understanding of the software engineering fundamentals required to build frameworks for AI.<br> Solid grasp of the underlying principles of document processing, vectorization, semantic search, and the architecture of Retrieval-Augmented Generation (RAG) pipelines.<br> Strong communication skills with the ability to collaborate with tech leads, product teams, dedicated DevOps engineers, and stakeholders across multiple departments.<br> Bonus points for: Knowledge of LLM observability, telemetry, tracing, and monitoring paradigms for production environments.<br> Conceptual understanding of modern infrastructure ecosystems (e.<br>g., Infrastructure as Code, container orchestration, GitOps, and automated deployment pipelines) to effectively collaborate with our DevOps team.<br> Experience integrating complex third-party SaaS APIs.<br> Ready to join the revolution?<br> We offer: A competitive compensation and benefits package.<br> The opportunity to make a real impact on a global scale.<br> A dynamic and supportive work environment that values your contributions.<br> Continuous learning and professional development opportunities to propel your career forward.<br> Show us what you've got!<br> Submit your resume, cover letter, and any relevant portfolio links showcasing your AI and automation expertise.<br> Infomineo: Where brilliant minds meet to shape the future of business.<br></span> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>About us Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence. We've evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise. We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling. This is made possible by relying on 3 pillars of excellence: 1) 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona), 2) Our proprietary AI orchestrator, 3) Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions. Ready to kick start your career with us? Become the AI &amp; Automation Rockstar at Infomineo! Do you dream of using cutting-edge AI and automation to revolutionize how businesses operate? Infomineo, a fast-growing business insights powerhouse, is seeking a Senior Research &amp; Development Engineer to join our ranks and spearhead the development of next-gen solutions for our global clientele. Why Infomineo? Here's what sets us apart: Shape the Future of Business Insights: You'll be at the forefront, designing and implementing AI-driven solutions that automate tasks and boost efficiency across our entire service spectrum (Business Research, Content, Design, and Data Analytics. Work with the Big Players: Our clients are industry leaders - Fortune 500s, consultancies, governments, and NGOs - and you'll play a vital role in supporting their success. Become an AI &amp; Automation Guru: We foster continuous learning. Expect to stay on top of the latest advancements and actively propose and implement improvements. Global Impact, Local Connections: Infomineo boasts a vibrant international team with offices across 5 continents. Collaborate with diverse, brilliant minds and gain exposure to a variety of industries and regions. Thrive in a Collaborative Culture: We value intellectual curiosity, creativity, and a "can-do" attitude. You'll be encouraged to contribute your ideas and make a real difference in the company's growth. Does this sound like your dream role? Here's what you'll bring to the table: 3-5 years of experience in backend development and distributed systems. Hands-on experiences in building scalable, high-throughput applications using robust architectural components such as messaging queues, caching layers, connection/resource pools, and event-driven asynchronous processing. Proven track record of contributing to the design and implementation of state-of-the-art software architectures, with a focus on clean code, design patterns, and maintainability for large-scale enterprise environments. Deep understanding of the software engineering fundamentals required to build frameworks for AI. Solid grasp of the underlying principles of document processing, vectorization, semantic search, and the architecture of Retrieval-Augmented Generation (RAG) pipelines. Strong communication skills with the ability to collaborate with tech leads, product teams, dedicated DevOps engineers, and stakeholders across multiple departments. Bonus points for: Knowledge of LLM observability, telemetry, tracing, and monitoring paradigms for production environments. Conceptual understanding of modern infrastructure ecosystems (e.g., Infrastructure as Code, container orchestration, GitOps, and automated deployment pipelines) to effectively collaborate with our DevOps team. Experience integrating complex third-party SaaS APIs. Ready to join the revolution? We offer: A competitive compensation and benefits package. The opportunity to make a real impact on a global scale. A dynamic and supportive work environment that values your contributions. Continuous learning and professional development opportunities to propel your career forward. Show us what you've got! Submit your resume, cover letter, and any relevant portfolio links showcasing your AI and automation expertise. Infomineo: Where brilliant minds meet to shape the future of business.</p></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>3-5 years of experience in backend development and distributed systems. Hands-on experiences in building scalable, high-throughput applications using robust architectural components such as messaging queues, caching layers, connection/resource pools, and event-driven asynchronous processing. Proven track record of contributing to the design and implementation of state-of-the-art software architectures, with a focus on clean code, design patterns, and maintainability for large-scale enterprise environments. Deep understanding of the software engineering fundamentals required to build frameworks for AI. Solid grasp of the underlying principles of document processing, vectorization, semantic search, and the architecture of Retrieval-Augmented Generation (RAG) pipelines. Strong communication skills with the ability to collaborate with tech leads, product teams, dedicated DevOps engineers, and stakeholders across multiple departments. Bonus points for: Knowledge of LLM observability, telemetry, tracing, and monitoring paradigms for production environments. Conceptual understanding of modern infrastructure ecosystems (e.g., Infrastructure as Code, container orchestration, GitOps, and automated deployment pipelines) to effectively collaborate with our DevOps team. Experience integrating complex third-party SaaS APIs.</p><p></p></section>