Machine Learning Engineer Expert

About the role

We are looking for an experienced Machine Learning Engineer to design, develop, and deploy large‑scale AI models on Azure. You will be instrumental in building a robust MLOps architecture that ensures high performance, reliability, and maintainability of production solutions.

Key responsibilities

• Design, train, and optimize ML/DL models using TensorFlow, PyTorch, and Scikit‑learn.
• Collaborate with Data Scientists to turn prototypes into production‑ready pipelines.
• Implement CI/CD pipelines for model training, evaluation, and deployment on Azure.
• Automate processes to guarantee continuous, reliable delivery.
• Improve inference performance, reduce latency, and optimize cost in production.
• Contribute to a comprehensive MLOps framework: data/model versioning, model registry, monitoring, and incident management.
• Document models, pipelines, and best practices for maintainability and compliance.
• Work closely with Data Engineering, DevOps, and multicultural agile teams.

Required profile

• Minimum 5 years of experience in Machine Learning, Data Engineering, or related fields.
• Proven track record of end‑to‑end model deployment, monitoring, and maintenance in production.
• Strong experience with Azure services (Azure ML, AKS, Data Lake, Data Factory, Databricks).
• Analytical mindset with rigorous technical approach.
• Excellent communication and collaboration skills in an agile, multicultural environment.
• Ownership‑driven, delivery‑oriented attitude.

Required skills

• Python, SQL, PySpark
• TensorFlow, PyTorch, Scikit‑learn
• MLflow, Kubeflow
• Azure ML, Azure Kubernetes Service (AKS), Azure Data Lake, Azure Data Factory, Databricks
• Docker, GitHub Actions, Azure DevOps
• Terraform (preferred)
• Understanding of distributed systems, data/model versioning, and scalable deployment practices