Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.Ready to make your next career move?
Explore the full job description below to learn more about the MLOPs Engineer role, its key responsibilities, and what it's like to be part of our team. If you're excited by what you see, we'd love to hear from you. Apply today and take the next step in your career journey.
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Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment
Collaborate with data scientists and software engineers to operationalize ML models,
serving frameworks (TensorFlow Serving, TorchServe) and experience with MLOps tools
Develop and maintain CI/CD pipelines for ML workflows
Implement monitoring and logging solutions for ML models, experience with ML model serving frameworks (TensorFlow Serving, TorchServe)
Optimize ML infrastructure for performance, scalability, and cost-efficiency
Your Profile
Strong programming skills in Python, with 5.5 to 14 years experience in ML frameworks; understanding of ML-specific testing and validation techniques
Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes), Knowledge of data versioning and model versioning techniques
Proficiency in cloud platform (AWS) and their ML-specific services with atleast 2-3 years of experience.
Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.)
Experience with monitoring and observability