MLOps (Machine Learning Operations) and Model accuracy Engineer (Navi Mumbai)

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way youd like, where youll be supported and inspired by a cooperative community of colleagues around the world, and where youll be able to reimagine whats possible. Join us and help the worlds leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Your Role Design and build scalable, reliable backend and platform systems optimized for ML workloads, ensuring high availability and performance Enforce strong engineering practices including modular design, automated testing, code quality, and scalability standards Develop and manage cloud-native infrastructure with Kubernetes, containers, and microservices, while optimizing for cost and resilience Establish and scale CI/CD pipelines across both application and ML lifecycles, with full observability (logging, metrics, tracing) Architect and implement end-to-end MLOps pipelines, from data ingestion through deployment, monitoring, and automated retraining Drive automation in model lifecycle management including versioning, experiment tracking, reproducibility, and governance using tools like MLflow, Kubeflow, and Airflow Define, monitor, and continuously improve model performance using key metrics (accuracy, latency, drift, bias), with robust evaluation and A/B testing frameworks Lead cross-functional teams and collaborate with stakeholders to deliver scalable AI solutions while shaping the ML platform strategy and adoption roadmap Your Skills Strong foundation in Software Engineering (Java/Python/Go) and system design Expertise in DevOps practices (CI/CD, Docker, Kubernetes, Infrastructure as Code) Proven experience in MLOps frameworks and model lifecycle management Deep understanding of model accuracy, evaluation metrics, and monitoring strategies Hands-on experience with cloud platforms (GCP/AWS/Azure) Prior experience managing engineering teams Why you will love .