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
