Embark on a career journey with Capgemini, a global leader in technology transformation. We empower our teams to shape their careers, providing support and inspiration within a collaborative worldwide community. Join us to help the world's leading organizations leverage technology for a more sustainable and inclusive future. Explore the MLOps Engineer role below and take the next step in your professional growth.

Design, implement, and maintain end-to-end ML pipelines for training, evaluation, and deployment. Collaborate with data scientists and engineers to operationalize ML models using serving frameworks like TensorFlow Serving and TorchServe. Develop and maintain CI/CD pipelines for ML workflows, and implement monitoring and logging solutions. Optimize ML infrastructure for performance, scalability, and cost-efficiency.

A strong command of Python and 5.5 to 14 years of experience with ML frameworks is essential. Expertise in containerization (Docker) and orchestration (Kubernetes) is required, alongside knowledge of data and model versioning techniques. Proficiency in cloud platforms, specifically AWS and its ML services (2-3 years experience), coupled with a deep understanding of DevOps practices and tools like GitLab, Artifactory, and Gitflow, is crucial. Familiarity with monitoring tools such as Prometheus, Grafana, and the ELK stack is also expected.