AI Engineer

Responsibilities:

• Design and build multi-step agents using LLMs, tool use, retrieval, and memory in frameworks like LangChain, LangGraph, or similar.
• Architect single-agent and multi-agent systems: subagents, routing and handoffs, planning, and state management for long-running tasks.
• Build evaluation frameworks for agent behavior, LLM-as-judge, and deterministic evaluators and use them to iterate on prompts and architecture.
• Integrate agents with client systems and data sources: APIs, databases, vector stores, and MCP or tool integrations.
• Deploy agents to production and set up observability, tracing, logging, and error analysis to monitor real-world behavior.
• Work directly with clients and domain experts to translate a workflow into an agent spec and iterate on it based on production feedback.

Requirements:

• 3-7 years of experience in backend or full-stack engineering, with a track record of shipping production systems.
• Hands-on experience building agents with LLM frameworks (LangChain, LangGraph, or similar), including tool use and multi-step reasoning.
• Experience with prompt engineering and evaluation frameworks and iterating on agent behavior using real feedback.
• Strong proficiency in Python and/or TypeScript, APIs, and system design; cloud experience preferred.
• Comfort taking ownership of a project end to end, from architecture to a client using it in production.
• High ownership, strong execution, and the instinct to improve systems without waiting to be asked.

Nice to Have:

• Experience with RAG patterns, vector stores, and knowledge retrieval.
• Experience with agent observability tooling (LangSmith or similar) and debugging agent behavior in production.
• Experience working directly with enterprise clients on technical scoping and delivery.
• Exposure to RLHF, SFT, or post-training workflows.