Capgemini - MLOps Engineer
Skills
About the role
This is a hands-on MLOps engineering role designing and operating multi-cloud ML and LLM infrastructure, open across Hyderabad, Pune and Bangalore. It suits a platform or DevOps engineer with several years of MLOps experience who wants to work on Kubernetes-based ML and LLM pipelines. The employer is Capgemini Technology Services.
What you’ll do
- Design and secure scalable ML infrastructure on AWS, Azure and GCP using Terraform
- Deploy and manage EKS/AKS/GKE clusters for ML and LLM workloads, GPUs and distributed training
- Operationalize LLM pipelines including fine-tuning, RAG, vector databases and inference serving
- Build automated CI/CD/CT pipelines for model training, packaging and deployment
- Implement monitoring for model latency, resource usage and data or concept drift
- Work with data science and platform teams to build self-service AI tools and enforce governance
What they’re looking for
- 5+ years in Platform/DevOps Engineering, with 3+ years in MLOps or AI Platform Engineering
- Deep hands-on experience with Kubernetes, Helm and Docker
- Strong Python development skills, including APIs via FastAPI or Flask
- Production experience with Terraform and CI/CD tools such as GitHub Actions or Azure DevOps
- Direct experience managing LLM pipelines, vector databases and inference optimization
- Hands-on setup of monitoring and model-drift alerting
- Proficiency in at least two major cloud platforms
Questions about this role
How many years of experience do I need?
5-8 years, with at least 5 years in Platform/DevOps Engineering and 3+ years dedicated to MLOps.
Which locations is this open in?
Hyderabad, Pune or Bangalore.
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