remote
MLops Engineer - Capgemini
MLOps Engineer
Lead the design, build, and operation of end‑to‑end MLOps pipelines, deploying and scaling AI/ML models in production while collaborating with data scientists and software teams.
About the role
Key Responsibilities
- Design, develop, and maintain scalable MLOps pipelines for model training, deployment, and monitoring.
- Collaborate with Data Scientists, AI Engineers, and DevOps teams to operationalize machine learning, NLP, and generative AI solutions.
- Implement CI/CD workflows, containerization (Docker), and orchestration (Kubernetes) for continuous delivery of ML models.
- Integrate model monitoring, logging, and alerting to ensure reliability and performance in production.
- Leverage cloud platforms (AWS, GCP, Azure) and tools such as MLflow, SageMaker, or Vertex AI for model lifecycle management.
- Document best practices, architecture decisions, and provide mentorship to junior engineers.
Requirements
- Strong experience with MLOps frameworks and tools (MLflow, Kubeflow, Airflow).
- Proficiency in Python and container technologies (Docker, Kubernetes).
- Hands‑on experience with CI/CD pipelines and cloud services (AWS, GCP, Azure).
- Solid understanding of model deployment, monitoring, and scaling strategies.
- Excellent communication skills and ability to work cross‑functionally.
Skills
mlopspythondockerkubernetescicdmlflowaws