Senior ML OPS Engineer

Your responsibilities:

• Lead the architectural evolution of our global recommender system, ensuring scalability, reliability, and high performance.

• Drive and implement MLOps best practices, including CI/CD pipelines (GitLab), monitoring, and MLflow-based experiment tracking.

• Partner with data scientists to productionalize machine learning models on AWS SageMaker.

• Act as a technical mentor for engineers and data scientists, promoting software engineering and deployment best practices.

• Contribute hands-on to our Python codebase, writing clean, maintainable, and well-tested code.

• Collaborate with product managers and business stakeholders to align ML solutions with business needs.

We are looking for you, if you have:

• 5+ years of experience as a Machine Learning Engineer, with a track record of deploying and maintaining production systems.

• Strong expertise in Python and familiarity with the ML/AI ecosystem (TensorFlow, PyTorch, etc.).

• Proven experience in AWS SageMaker for building, training, and deploying models.

• Hands-on knowledge of MLOps tools: MLflow (tracking & model management), GitLab CI/CD (automation), Docker/Kubernetes.

• Experience designing and operating scalable ML pipelines in a cloud environment (AWS preferred).

• Strong communication skills and ability to mentor and guide data scientists and engineers.

Nice to have:

• Master’s or PhD in Computer Science, AI/ML, or related field.

• Experience with model monitoring & observability tools (Prometheus, Grafana, Evidently AI).

We offer:

• Participation in interesting and challenging projects

• Flexible working hours

• A great, non-corporate atmosphere

• Stable employment conditions (contract of employment or B2B contract)

• Opportunities for development and promotion

• Attractive package of benefits

• Remote work

We reserve the right to contact the selected candidates.