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Agent Engineer

Sarvam· Deployment Engineering
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BengaluruFull-timePosted 2mo ago

About the role

Agent Engineers build and operate production-grade AI agents integrated into enterprise systems, with a focus on scalable, measurable quality, fault-tolerant design, and end-to-end ownership from concept to live operation.

Responsibilities

  • Build production agents on the Sarvam stack: prompts, system design, workflows, tool integrations, and evals.
  • Map the space the agent has to cover. Work out the scenarios that matter, how each should be handled, and where the edges are, so coverage is designed rather than discovered later.
  • Build the proof of concept when a customer wants to see it work before committing at scale, and get to a convincing result fast.
  • Integrate into systems that were never designed for this: core banking, CRM, ticketing, telephony, and whatever else the customer runs.
  • Own quality end to end. Define what good means for the use case, build the evals that measure it, and close the gap until the numbers hold.
  • Engineer agents like software. Version them, review them, and hold them to a regression suite, so quality does not depend on who last edited a prompt.
  • Tune for the constraints that decide whether a system survives production: accuracy, cost, reliability, and performance under real load.
  • Design for scale and consistency from the outset. Systems that behave the same on the hundred-thousandth interaction as the first, and that are instrumented well enough for anyone to see when they are not.
  • Take a proven pattern into a new customer and make it fit their environment, at pace and at quality.
  • At senior levels, own the technical build playbooks and the technical quality bar for a motion, and bring partner engineers up to it.

Qualifications

  • You have shipped to production and owned it afterwards.
  • 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. You are uncomfortable when a system's behaviour depends on a prompt nobody can account for.
  • You take evals seriously. You can define metrics that matter for a use case, build the pipeline, and tell the difference between a real signal and noise.
  • Strong Python and range across the stack. Backend services, data pipelines, APIs, cloud infrastructure. When you meet an unfamiliar stack you pick it up in hours.
  • You design for the long run. You build systems that hold their behaviour over months, not just on launch day: predictable under real load, instrumented so problems are visible, and structured so someone else can reason about them.
  • You can sit with a customer's engineering team. Understand their constraints, explain yours, and get to a workable answer without either side losing patience.

Skills

Required:Production-grade software engineering for AI agentsLLM/tool integrationPython
Last updated Sep 1, 2026. Interested? Apply on the company site.
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About Sarvam

Sarvam | India's Full-Stack Sovereign AI Platform

Sarvam is India's full-stack sovereign AI platform, with speech-to-text, text-to-speech, translation, and conversational agents across 22 Indian languages. Start free.

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