Advance your career in ML Engineering with Capgemini, a global leader in technology transformation. We empower you to shape your professional journey within a supportive, collaborative environment.

Join a team dedicated to helping the world's leading organizations leverage technology for a more sustainable and inclusive future. This role is key to unlocking the full potential of AI and advanced technologies.

Design, implement, and manage comprehensive ML pipelines for model training, evaluation, and seamless deployment.

Collaborate with data scientists and software engineers to operationalize machine learning models, utilizing serving frameworks like TensorFlow Serving and TorchServe.

Develop and maintain robust CI/CD pipelines specifically for ML workflows, ensuring efficient and reliable model updates.

Implement effective monitoring and logging solutions for ML models and optimize ML infrastructure for peak performance, scalability, and cost-efficiency.

A strong foundation in Python programming and 5-14 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, particularly AWS and its ML services (minimum 2-3 years experience), is critical.

Demonstrated understanding of DevOps practices and tools such as GitLab, Artifactory, and Gitflow is necessary. Experience with monitoring tools like Prometheus, Grafana, and the ELK stack, plus knowledge of distributed training techniques, is highly valued.