Must have

• Python

• AWS

• MLOps

• Machine learning

• English (C1)

Requirements description

Must-Have Requirements:

• 5+ years of experience as a Machine Learning Engineer with a proven track record of deploying production ML models
• Strong knowledge of: Python, MLflow, GitLab CI/CD, AWS SageMaker
• Experience working with TensorFlow or PyTorch
• Ability to design, document, and communicate the architecture of ML and data systems
• Practical experience with the full MLOps lifecycle
• Strong collaboration skills and experience mentoring technical teams
• Experience working in a major cloud environment – ideally AWS

Nice to Have:

• Master’s or PhD in ML, AI, or a related technical field
• Familiarity with model monitoring tools: Prometheus, Grafana, Evidently AI
• Excellent communication skills and ability to explain technical decisions to various stakeholders

Offer description

About the Project:

Join a global project focused on the development and maintenance of an advanced recommender system that is already generating real business value across multiple countries. Your mission will be to elevate the system’s architecture and operational maturity. This is a technical leadership role, combining hands-on engineering with MLOps expertise and production-grade ML deployment.

Terms:

• B2B contract
• Fully remotely
• Long-term
• Working on your own devices

Your responsibilities

1. Design and evolve the architecture of a live recommender system
2. Lead the MLOps strategy and implementation (GitLab CI/CD, monitoring, MLflow)
3. Collaborate with Data Scientists to deploy machine learning models to production using AWS SageMaker
4. Mentor and advise the team on ML engineering and DevOps best practices
5. Contribute hands-on to the Python codebase – ML + data infrastructure + deployment
6. Translate business needs into concrete technical solutions

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