About KIM

KIM is an AI-native services company building AI-powered customer support for D2C e-commerce brands — work done by AI, vetted by humans. Our core AI system handles real customer conversations for real brands, every day. It's not a demo. When it gets something wrong, a customer notices.

The Role

You'll work directly alongside our senior AI engineer on our hardest AI problems. This role leans more toward applied research than conventional backend engineering — you'll spend your time on questions like:

How do we know a system change actually made responses better? (Eval design, harness construction, failure taxonomies)

Where should reasoning be deterministic vs. LLM-driven?

Why did retrieval miss, and how do we fix the class of failure rather than the instance?

How should an AI system remember things over long-running relationships?

You'll design experiments, build eval datasets that reflect real-world distributions (not cherry-picked queries), diagnose regressions, and argue for architectural decisions with evidence.

Who We're Looking For

AI-forward. You follow the field closely because you want to, not because you have to. You've formed opinions about what works and what's hype.

Strong intuition for modern AI and backend systems. You understand LLM pipelines, retrieval systems, and agent architectures well enough to reason about their failure modes — and enough backend fundamentals to ship what you design.

Comfortable in ambiguity. Problems here rarely arrive well-specified.



You can take a vague concern ("the new version feels worse") and turn it into a measurable question.

Thinks in abstractions. You naturally lift a problem to the right level — you see the class of failure, not just the instance — and you have taste and conviction about how things should be built.

Pushes back. You disagree openly, argue from first principles, and change your mind when the evidence says so. We want proper discussions, not silent agreement.

Builds for fun. You have personal projects — things you made because you were curious. We'd genuinely like to see them.

Great to Have

Hands-on experience with evals — building harnesses, designing datasets, measuring LLM output quality, catching eval inflation and dataset contamination.

Experience with RAG systems in production: retrieval debugging, reranking, chunking strategy.

Familiarity with agentic patterns (ReAct-style loops, tool use, orchestration) and where they break.

Prior work at an AI-first startup, research lab, or responsible open-source AI contributions.

What You'll Get A seat next to the people making the core architectural decisions — small team, no layers, direct access to founders.

Real production feedback loops: your experiments ship to live customer conversations, and you see the results in days, not quarters.

A research-flavored role with the accountability of a product company — the best of both. Drop your CV at [email protected]

📌 Applied AI Engineer (Bengaluru)
🏢 kim.cc
📍 Bengaluru