About The Role

Own the platform our AI runs on. You will make deployment boring, cost visible, and model updates something we test rather than discover.

What You'll Do

Build and operate model serving infrastructure

Implement monitoring for quality drift, latency and spend

Build CI pipelines that run evaluations before a model change ships

Manage model registries and rollback paths

Optimise inference cost through routing, caching and batching

Support private and self-hosted deployments for regulated clients

What You'll Bring

2+ years in DevOps, platform or ML infrastructure

Strong Docker and cloud experience on AWS or GCP

Python and comfort with CI/CD tooling

Understanding of GPU cost and capacity planning

Monitoring and alerting instincts

Nice to Have vLLM, Ray or similar serving stacks

Kubernetes in production

Experience self-hosting open-weight models





What You'd Build

These aren't hypothetical projects, they're live products you can try before your first interview.

The StackBinary MarTech Suite, The full product line, every system we ship, most with live demos.

Why Join StackBinary™?

Versatile working hours

Remote-friendly culture

Learning & development budget

High-ownership projects

Pragmatic engineering culture

Work with cutting-edge tech

Ready to Apply?

Join our team of builders who love shipping quality software.

We connect with shortlisted candidates through LinkedIn or our official email IDs.

Follow StackBinary on LinkedIn →

Questions about this role?

[email protected]

📌 MLOps Engineer (AI Infrastructure) (Mumbai)
🏢 Stackbinary
📍 Mumbai