We’re hiring an MLOps Engineer to own the infrastructure our AI runs on.

Our AI systems are already in production and already touching real loans. The next chapter is making them scale, deploy safely, and change quickly — infrastructure that absorbs real production load, a promotion path that makes shipping boring, and internal tooling that lets the team improve AI behaviour without an infrastructure engineer in the loop for every change.

This is not a role where you inherit a mature platform and keep the lights on. You’ll take AI services that were built to prove the idea and turn them into systems that hold up under load, deploy predictably across environments, and give the rest of engineering a safe, quick path to production. You own the arc from “this works on one machine” to “this is a platform the whole company builds on.”

This role is right for you if:
• You’ve taken AI or ML systems from a single deployment to infrastructure that scales — and you’ve been on call for the result
• You treat infrastructure as a product with users, not a ticket queue




• You think deploys should be boring, reversible, and frequent — and you’ve built the systems that make that true
• You’d rather remove yourself from the critical path than be the person everyone has to ask

What you’ll build

Our AI workloads are moving from early-stage deployment to production scale. You’ll design what they run on:
• Re-architect how AI services are deployed and run — from single-host setups to horizontally scalable, orchestrated infrastructure that absorbs traffic spikes without degrading
• Design for the specific realities of LLM-backed workloads: long-running requests, streaming responses, bursty concurrency, expensive downstream calls, and upstream rate limits
• Own capacity, autoscaling, and unit economics — you should be able to say what we spend per unit of work, and why

Shipping AI changes should be a routine, reviewable event — not a coordinated risk:
• Build the promotion path from de

📌 MLOps Engineer (India)
🏢 Saaf Finance
📍 India