Function: AI Engineering / Applied AI Delivery

About The Role

We're hiring an AI Engineer to design, build, and ship production AI systems — not prototypes, not notebooks. This is a builder's role: you'll own the path from "we think an LLM/agent could solve this" to a system running reliably in production, under real load, with real failure modes.

We are being deliberately selective here. This role is not for someone who has "used ChatGPT a lot" or built a weekend RAG demo. We want people who have shipped agentic or LLM-powered systems that other engineers depend on, who understand why those systems break, and who can hold their own in a room full of skeptical senior engineers. If that's not you yet, this probably isn't the right role yet either — and that's fine.

Requirements

What You'll Own

Design and build production-grade AI/agentic systems — from architecture through deployment, monitoring, and iteration — not just model calls wrapped in a script
Own the full lifecycle of at least one non-trivial AI capability: problem framing, evaluation strategy,



prompt/context engineering, orchestration, deployment, and post-launch tuning
Build deterministic guardrails around probabilistic components — retries, validation, fallback paths, human-in-the-loop checkpoints where confidence is low
Design evaluation harnesses and offline/online eval pipelines that actually catch regressions, not vanity metrics
Make real architectural tradeoffs on latency, cost, and reliability — token economics is a design constraint you think about upfront, not an afterthought
Integrate AI systems into existing production infrastructure (APIs, data pipelines, orchestration layers) without breaking what already works
Push back on bad ideas — including ours — with technical reasoning, not opinions

Who You Are

4+ years of robust software engineering experience, with at least 1-2 years hands-on building and shipping LLM/agentic systems in production (not just experimentation)
Fluent in Python

📌 AI Engineer (Pune)
🏢 Capgemini
📍 Pune