About the project

• We are looking for an experienced Senior MLOps Engineer to join a long-term project in the AI/ML domain. The role involves designing, building, and maintaining robust MLOps pipelines to ensure the efficient deployment, monitoring, and governance of machine learning models in production environments. You will work closely with Data Science, Machine Learning, and Infrastructure teams to enable smooth, secure, and scalable ML operations in a multi-cloud setting.

Your responsibilities

• Design, build, and scale MLOps pipelines for training, evaluation, versioning, and deploying ML models.
• Manage containerized environments using Kubernetes and Docker.
• Orchestrate workflows with Airflow, MLflow, Kubeflow, or similar tools.
• Deploy and monitor ML models on AWS SageMaker, Azure ML, or Google Vertex AI.
• Implement CI/CD pipelines with a strong focus on security, reproducibility, and reliability.
• Collaborate with Data Science, ML, and Infrastructure teams for seamless model handover.
• Implement ML observability practices: model drift detection, latency monitoring, and performance metrics tracking.
• Support governance, feature store integration, and reproducibility standards within the organization.

Our requirements

• Proven experience in MLOps, DevOps for AI, or ML platform engineering.
• Proficiency in Kubernetes, Docker, and workflow orchestration tools.
• Strong programming skills in Python.
• Hands-on experience with Infrastructure-as-Code tools (e.g., Terraform, Helm).
• Production-grade ML model deployment experience using AWS SageMaker, Azure ML, or Google Vertex AI.
• Understanding of data pipelines, feature stores, and cloud architectures.
• Proficiency in CI/CD for ML, including versioning, testing, and secure deployment.
• Strong teamwork, problem-solving, and communication skills.

Optional

• Experience with monitoring tools such as Prometheus and Grafana.
• Familiarity with ML observability platforms.
• Knowledge of compliance, governance, and model risk management in regulated environments.

Technologies we use