Applied AI Engineer - India

Own the production improvement loop across agent behavior, customer and operator feedback, evaluation, experimentation, and verified business outcomes

Instrument agent workflows so model interactions, tool use, decisions, failures, human edits, and downstream outcomes can be understood in context

Define meaningful quality standards, representative evaluation datasets, regression coverage, and production monitoring.

Investigate why agents underperform across context, knowledge, instructions, tools, routing, guardrails, or workflow design

Design and ship targeted behavior improvements, including changes to prompting, context construction, decision logic, tool use, and human-review paths

Build backend services, APIs, data models, and feedback pipelines that make agent behavior observable, steerable, and reproducible

Run controlled experiments, production replays, or staged rollouts to measure whether changes improve quality and downstream business results

Partner with Product, Data Science, and Sales to prioritize high-value problems and define customer and business success

Ship with appropriate safeguards for privacy, security, reliability, human oversight, and safe operational rollout

2-5 years of software engineering experience, with hands-on experience building or operating LLM-powered features or agents in production - not just prototypes or demos

Fluency with prompting and context engineering as an engineering discipline: you iterate on prompts, context construction, and tool definitions the way you'd iterate on code

Experience building or maintaining evaluation harnesses for AI systems: offline eval sets, LLM-as-judge or human-in-the-loop scoring, regression detection

Genuine comfort with non-determinism: you reason about agent behavior across a distribution of production traffic, not a fixed set of test cases, and you don't treat variance as a bug to be argued away