MLOps Engineer (Machine Learning, MLFlow, Kubernetes, DVC)

Capgemini is an AI-powered global business and technology transformation partner with nearly 60 years of heritage, employing 420,000 team members globally. The Cloud & Custom Applications (C&CA) team provides end-to-end IT services covering business specification, software development, implementation, and maintenance, focusing on DevOps, SRE, automation, and AI to improve delivery efficiency, quality, and reliability.

• Master’s, engineer’s or bachelor’s degree.
• At least 4 years of experience in CI/CD and/or cloud technologies.
• Experience with Git, CI/CD tools, Kubernetes, Docker, ML frameworks such as TensorFlow or similar, monitoring and logging tools, and Infrastructure as Code.

Nice to have:

• Practical experience with MLflow, DVC or similar tools supporting model lifecycle management.
• Hands-on experience in AI / GenAI, including practical use of LLMs, RAG patterns and agentic workflows.
• Knowledge of vector search, embeddings, model-serving patterns or AI observability.
• Good written and verbal communication skills, minimum B2 English.

• Ensure that machine learning models are efficiently deployed, maintained and monitored across environments.
• Automate deployment and update processes for machine learning models.
• Implement CI/CD pipelines for ML models and ML workflows.
• Set up monitoring, logging and observability for ML models and model-serving platforms.
• Optimize ML models and model delivery processes for production or near-production environments.
• Support AI and GenAI initiatives, including LLM-based solutions, RAG patterns and agentic workflows where relevant.
• Help establish reliable MLOps practices for model versioning, reproducibility, governance and responsible AI delivery.