Overview:

As an experienced Machine Learning Engineer, you will be responsible for designing, developing, deploying, and optimizing large-scale AI models to meet business needs. You will play a key role in establishing a robust, scalable MLOps architecture, ensuring high performance, reliability, and maintainability of production solutions on Azure cloud.

Key Responsibilities:

Model Design and Development:

• Design, train, and optimize Machine Learning and Deep Learning models using frameworks such as TensorFlow, PyTorch, and Scikit-learn. Collaborate with Data Scientists to turn prototypes into production-ready solutions.

Industrialization and Deployment:

• Implement CI/CD pipelines for training, evaluation, and deployment of models on Azure. Automate these processes to ensure continuous, reliable delivery.

Performance Optimization in Production:

• Improve model inference performance, reduce latency, and optimize costs. Make adjustments to ensure scalability and robustness.

MLOps and Cloud Architecture:

• Contribute to building a comprehensive MLOps architecture, including versioning data and models, model registry, monitoring, and incident management.

Documentation and Best Practices:

• Document models, pipelines, and processes to ensure maintainability, reusability, and compliance with company standards.

Collaboration and Communication:

• Work closely with Data Science, Data Engineering, and DevOps teams in an agile, multicultural environment to deliver high-value solutions.

Technical Skills Required:

• Programming Languages: Python, SQL, PySpark
• ML Frameworks and Tools: TensorFlow, PyTorch, Scikit-learn, MLflow, Kubeflow
• Cloud Platforms: Azure (Azure ML, AKS, Data Lake, Data Factory, Databricks)
• DevOps & Automation: Docker, GitHub Actions, Azure DevOps, Terraform (preferred)
• Distributed Architecture: Strong understanding of distributed systems, data/model versioning, and scalable deployment practices

Experience:

• Minimum of 5 years in Machine Learning, Data Engineering, or related fields
• Proven experience in end-to-end model deployment, monitoring, and maintenance in production
• Cloud experience, ideally with Azure, for implementing MLOps solutions

Soft Skills:

• Analytical mindset with strong technical rigor
• Excellent communication and collaboration skills
• Ability to work in agile, multicultural environments, taking ownership of projects
• Delivery-oriented with a focus on ownership and results