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.