AI Engineer - Core

These are the kinds of problems you'll walk into on day one:

Intelligent retrieval across heterogeneous approaches — our agents need the right information at exactly the right moment. The challenge isn't picking one retrieval method; it's combining RAG, graph-based retrieval, and other approaches into a unified strategy that fetches the most relevant content precisely when the agent needs it — no more, no less.

Agentic workflows that solve real-world problems — it's building workflows robust enough to handle the unexpected. When an agent hits an edge case, missing data, or a situation it wasn't explicitly designed for, it needs to reason through it — leveraging available context, escalating to a human when it can't, and never silently failing.

Evaluation beyond vibes — we need systematic, reproducible evals that actually predict real-world performance. If you've built custom evaluators for RAG or agent workflows, we want to talk.

Execution and real-world integration — an agent that only surfaces insights isn't enough. We're building systems where agents take action — integrating with external platforms, executing workflows, and doing real work with the information they have, combined with human-in-the-loop checkpoints that keep enterprise trust intact.

We care about how you think and how you ship - not how many years are on your resume.

You're a strong Software engineer. Your code is clean, testable, and production-ready.

You have real experience with LangChain, LangGraph, or equivalent agent/orchestration frameworks. You've built with them, hit their limits, and worked around them - not just followed tutorials

You communicate with clarity and conviction. You can explain a technical decision to a non-technical founder and debate architecture tradeoffs with a senior engineer . Communication is not a nice-to-have here - it's core to the role

You take ownership. You don't wait for tickets. You see what needs to be built, raise your hand, and ship it

You thrive in ambiguity. AI products evolve fast. Requirements change. You're energized by figuring it out.

You move at startup speed. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment

Experience building evals pipelines — designing metrics, running systematic evaluations, and using results to drive iteration on AI systems

Backend software engineering experience — building APIs, services, data infrastructure, or production systems

Exposure to retrieval-augmented generation (RAG) , vector databases, or LLM-powered search and recommendation systems

Experience at early-stage startups or high-growth environments where you wore multiple hats

A backend engineer who went deep on LLMs and never looked back. An ML engineer who realized they love building products, not just models. A startup CTO who wants to go deep on AI at a company where the stack is the product. Someone who's been hacking on agents and pipelines nights and weekends and wants to do it full-time with real enterprise stakes. What matters: you ship, you own it, and you communicate like a teammate — not a silo.