Job description

Your responsibilities:

• Lead architecture improvements for a live recommender system

• Define and implement MLOps strategy and tools (CI/CD, MLflow, monitoring)

• Deploy and optimize ML models on AWS SageMaker

• Mentor data scientists and engineers on best practices

• Write clean, testable Python code for ML infrastructure

• Collaborate with product and data teams on technical solutions

• Drive adoption of modern, scalable ML technologies

We are looking for you, if you have:

• 5+ years in ML engineering or MLOps roles

• Strong Python skills and knowledge of the data science ecosystem

• Hands-on experience with AWS SageMaker and ML model deployment

• Proficient in GitLab CI/CD, MLflow, and cloud (preferably AWS)

• Experience with Docker, Kubernetes, and IaC tools (Terraform, Ansible)

• Deep-learning framework knowledge (TensorFlow or PyTorch)

• Strong understanding of ML pipeline design and architecture

• Experience mentoring technical teams

• Good communication and collaboration skills

• English at B2+ level

Preferred:

• Master’s or PhD in ML, AI, or related field

• Familiarity with advanced monitoring tools (Grafana, Prometheus, Evidently AI)

• Experience with production-scale recommender systems

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.