Agent Engineer

Overview

The distance between an AI system that works and one that millions of people rely on every day is where the real engineering lives. Crossing it is what Agent Engineers at Sarvam do. The agents you build go into live operation at India's largest banks, insurers, NBFCs and government departments, and at some of the country's fastest-growing companies, handling millions of real interactions, measured against outcomes th

What you'll do

• The distance between an AI system that works and one that millions of people rely on every day is where the real engineering lives.
• Crossing it is what Agent Engineers at Sarvam do.
• The agents you build go into live operation at India's largest banks, insurers, NBFCs and government departments, and at some of the country's fastest-growing companies, handling millions of real interactions, measured against outcomes the customer wants to drive.
• The hard part is not the first version.
• It is everything after: mapping the full space of what an agent has to handle so the scenarios that matter are covered by design rather than discovered in production, engineering for the failure modes instead of the happy path, and building the evals and instrumentation that make quality measurable, so drift surfaces in the data rather than in a customer escalation.
• Not prompts someone tweaks in a console, but engineered artifacts: versioned, reviewed, and held to a regression suite, where you can reason about what a change will do and roll it back if you are wrong.

Requirements

• What matters more than years is whether you have built something real users depended on and kept it working.
• You have shipped to production and owned it afterwards.
• Kept it running, fixed it at 11pm, and let real usage change your assumptions.
• You have worked with LLMs and agents seriously.
• Context engineering, tool use, structured outputs, memory, and the failure modes that only show up at scale.
• You have used agentic frameworks and both open and closed source models.
• You bring engineering discipline to AI.
• You think in versions, tests, regressions and reproducibility.

Skills