AI Engineer

Build LLM-powered features that are reliable, evaluated, and safe in production. You understand the difference between a cool demo and a dependable product.

Requirements

Shipped at least one production feature backed by an LLM or ML model
Deep comfort with the modern LLM stack — prompting, tool use, structured output, streaming
Experience writing evals (offline and online) for non-deterministic systems
Strong software engineering fundamentals (TypeScript or Python)

Responsibilities

Design prompts, tool schemas, and agent loops for real product workflows
Build evaluation harnesses and regression suites for model-backed features
Measure and improve latency, cost, and quality tradeoffs
Handle safety concerns — prompt injection, PII, data leakage

Nice to haves

Familiar with RAG pipelines and vector stores (pgvector, Pinecone, etc.)
Experience with fine-tuning or DPO/RLHF workflows
Contributed to an agent framework (LangChain, LangGraph, DSPy, or similar)

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