Lead MLOps Engineer (Gen AI)

We are looking for a Lead MLOps Engineer to build and scale the infrastructure powering enterprise ML and GenAI applications. The role will own the end-to-end MLOps ecosystem, from model training and deployment pipelines to Kubernetes, cloud/on-prem infrastructure, self-hosted LLMs, monitoring, security and production reliability.

This is a hands-on technical leadership role requiring strong experience across MLOps, DevOps and enterprise infrastructure, along with the ability to lead a small engineering team.

Responsibilities:

• Design and build scalable MLOps pipelines for training, deployment and lifecycle management of ML/GenAI models.
• Build infrastructure for self-hosted LLMs and GenAI applications across cloud and on-prem environments.
• OwnKubernetes-based orchestration, containerization and production deployment of ML workloads.
• Manage infrastructure across AWS, Azure, GCP and on-prem environments.
• Build and improveCI/CD, model deployment, automation and release pipelines.
• Set up monitoring for model performance, infrastructure health, latency, availability and resource utilisation.
• Drive infrastructure scalability, capacity planning, reliability, security and cost optimisation.
• Work with platforms/tools such as Kubeflow, ONNX, Docker, Kubernetes, Kong and JFrog.
• Partner closely with Data Scientists, AI Engineers, Data Engineers and Backend teams to move models from experimentation to production.
• Lead and mentor MLOps/DevOps engineers while remaining technically hands-on.

Requirements:

• 8+ years of overall experience with strong hands-on exposure to MLOps / DevOps / Cloud Infrastructure.
• Strong expertise in Kubernetes and Docker in production environments.
• Experience deploying ML/GenAI workloads across AWS, Azure, GCP and/or on-prem infrastructure.
• Strong understanding of ML training and inference infrastructure, deployment pipelines and production model lifecycle.
• Experience supportingself-hosted LLMs / GenAI modelsin enterprise environments.
• Hands-on experience with tools such as Kubeflow, ONNX, Kong, JFrog, or similar platforms.
• Strong understanding ofCI/CD, infrastructure automation, monitoring, security and high availability.
• Experience leading MLOps / DevOps engineers and owning large-scale production deployments.
• Strong stakeholder management and collaboration skills.
• B. E. / B. Tech / M. Tech / MCA or equivalent technical qualification.

Preferred:

• Experience working in Banking / FinTech or other large-scale enterprise environments.
• Experience managing systems spanning both cloud and on-prem infrastructure.
• Exposure to production GenAI platforms, LLM inference infrastructure and GPU-based workloads.