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Software Engineer (AI Agents)

Posted a month ago
2-5 years experience
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AI Summary

Design, build, and ship production-grade AI agents for enterprise planning. Develop supporting infrastructure including data pipelines, retrieval systems, and evaluation harnesses.

About the job

We form org-wide codebase intelligence - helping teams plan, build, and ship the software the world runs on. We outscored DeepWiki by 15%, Google Code Wiki by 38%, and Claude Code by 40% on AI codebase documentation benchmarks.

Now we're building AI agents for one of the hardest problems in enterprise software: planning. The systems you build will reason about high-stakes decisions for billion-dollar enterprises, where being wrong is expensive and being right is transformative.

This is not a wrapper-around-an-API job. You'll write production-grade code that holds up at scale and design agents that work when reality gets messy.

What you'll do

  • Design, build, and ship production AI agents for enterprise planning.

  • Make deliberate architectural choices: planning loops, tool use, memory, multi-agent, human-in-the-loop - and know when not to use each.

  • Write production-grade Python that scales and stays maintainable.

  • Build supporting infrastructure: data pipelines, retrieval, evaluation harnesses.

  • Iterate on reliability, accuracy, and cost.

What we're looking for

  • 2+ years of professional software development.

  • Strong Python with a track record of production code at scale, not just prototypes.

  • Hands-on experience building AI agents, not just using them.

  • Real understanding of where LLM-based agents excel and where they fail.

  • Familiarity with ReAct, planning/reflection loops, tool use, multi-agent orchestration, RAG-augmented agents, and the tradeoffs.

  • MongoDB: schema design, queries, production use.

Nice to have

  • Agent frameworks (LangGraph, LlamaIndex, CrewAI, AutoGen) - and comfort without them.

  • Background in planning, scheduling, optimization, or operations research.

  • Evaluation and observability tooling for non-deterministic systems.

  • Enterprise software exposure.

Who you'll work with

A small, senior team - the co-founders, every day. Abhishek (CEO) led AI Agents at Leena AI (YC S18) from $100K to $10M ARR. Nilesh (COO) was a Director at a Series B company and learned ML at CMU. Our mentor, advisor, and investor Mitz Banarjee backs Anthropic, SpaceX, xAI, Perplexity, Groq, Cerebras, and Figure - and helped take Workiva (NYSE: WK) from founding to IPO.


How we work

Core team, real ownership, early-team ESOPs on the table.

Remote-first. Gurugram preferred for occasional in-person.

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