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Back Posted on 25/06/2026

MLops Engineer

Capgemini Pune

Applications close on July 25, 2026

  • Corporate Common and Invest
  • Software Engineering

Software Engineering | Full Time | Experienced Professionals

Job Description

Your Role

  • Design, build, and manage end-to-end MLOps pipelines to deploy, monitor, and scale AI/ML models in production environments.
  • Collaborate with Data Scientists, AI Engineers, and Software Development teams to operationalize machine learning, NLP, and Generative AI solutions.
  • Develop and maintain infrastructure for model training, validation, deployment, monitoring, and automated retraining.
  • Implement CI/CD and automation frameworks to accelerate model releases while ensuring reliability and governance.
  • Manage containerized and cloud-based deployments using technologies such as Docker, Kubernetes, AWS, Azure, and GCP.
  • Drive model performance, scalability, security, and observability through continuous monitoring, optimization, and troubleshooting.

Your Profile

  • 4+ years of hands-on experience deploying and managing Machine Learning models in production environments.
  • Strong expertise in Python and ML/DL frameworks such as TensorFlow, PyTorch, and scikit-learn, along with MLOps platforms like MLflow, Kubeflow, or TFX.
  • Experience implementing CI/CD pipelines using tools such as Jenkins, GitLab CI, or similar DevOps technologies.
  • Solid understanding of cloud platforms (AWS, Azure, GCP), containerization (Docker), orchestration (Kubernetes), and version control systems (Git).
  • Excellent problem-solving, communication, and stakeholder management skills, with the ability to work effectively across cross-functional teams.

What you will love at capgemini

  • Opportunity to work on cutting-edge AI, Generative AI, Machine Learning, and cloud transformation programs for global clients.
  • Collaborative and inclusive work environment that encourages innovation, learning, and knowledge sharing.
  • Access to industry-leading technologies, cloud platforms, and modern MLOps ecosystems.
  • Continuous learning opportunities through certifications, technical academies, and career development programs.
  • Exposure to large-scale enterprise AI implementations, enabling you to make a real business impact.
  • Flexible and people-centric culture focused on growth, well-being, and long-term career progression.