Technical Lead – ML Platform & MLOps

About the role

You will work on some of the hardest engineering problems at the intersection of Machine Learning, Distributed Systems, Kubernetes, GPU infrastructure, Cloud, Developer Platforms and SRE. The platform you build will enable Data Scientists and ML Engineers to move from an idea to production faster: Code → Features → Distributed Training → Experimentation → Model Registry → Deployment → Inference → Observability

What you'll do

• Architect, build and evolve scalable ML Platform and MLOps capabilities for large-scale production workloads
• Create self-service infrastructure that allows Data Scientists and ML Engineers to train, experiment, deploy and operate models without managing underlying infrastructure
• Design reusable platform abstractions, SDKs, APIs and tooling that dramatically improve ML developer productivity
• Build systems for reproducibility, lineage, versioning, governance and lifecycle management across ML workflows
• Distributed Training & GPU Infrastructure

What you need

• 6–11 yrs of experience
• Machine learning
• MLOps
• Python
• Airflow
• Kubernetes
• Terraform
• Any graduate

Good to have

• Databricks
• Grafana
• System design
• CI/CD
• Linux
• LLMs