Description

Lead the ML Platform engineering team and own the architecture, roadmap, and delivery of scalable MLOps solutions for end-to-end machine learning workflows.

This role is on-site.

Responsibilities

• Lead, mentor, and grow the ML Platform engineering team, driving technical excellence and ownership.
• Design and implement scalable MLOps solutions, CI/CD pipelines, and automation frameworks for ML deployment, monitoring, and rollback.
• Own the architecture and roadmap for ML platforms, ensuring reliability, performance optimization, security, and cost efficiency.
• Conduct architecture reviews and provide hands-on guidance to engineering teams on cloud-native and Kubernetes-based best practices.
• Partner with ML engineers, data scientists, and product managers to deliver successful solutions and ensure operational excellence in production.

Required Skills

• 10+ years of overall experience, including 3-4+ years in an Engineering Manager or Technical Manager role.
• Strong hands-on experience with AWS cloud services, Kubernetes, Amazon SageMaker, Kubeflow, and MLflow.
• Proven expertise in end-to-end machine learning lifecycle management, including training, deployment, and monitoring.
• Deep knowledge of MLOps platform engineering, infrastructure design, and CI/CD pipelines for ML operations.
• Experience leading engineering teams, including hiring, performance management, and career development.
• Strong background in cloud-native architecture, automation practices, and scalable system design.
• B.Tech or equivalent education.

Preferred Skills

• Experience with data platforms and monitoring frameworks for production-grade ML environments.

Key Skills

Education

Any Graduate