AI Agent Architect
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Summary
Design and build AI-agent workflows, integrating models, memory, and tools; set reliability standards and evaluation tooling for production systems.
You will design and build AI-agent workflow architecture, including planning, tool use, memory, retrieval, and human checkpoints. You will integrate and fine-tune models, establish reliability and observability standards, build evaluation tooling, and make documented architectural decisions for production systems.
Responsibilities
- Design and build AI-agent workflow architecture
- Evaluate, integrate, and fine-tune foundation models and LLM APIs
- Define standards for agent reliability, observability, and failure modes
- Translate client deployment learnings into reusable platform components
- Build evaluation harnesses for agent quality, hallucination rates, and task completion
- Make documented architectural decisions
Requirements
- 6–10 years building production AI or data systems
- Experience with multi-agent architectures
- Strong Python skills
- Experience with agent frameworks such as LangChain, LlamaIndex, or AutoGen
- Experience with RAG architectures and vector databases
- Experience deploying LLM-powered systems in enterprise contexts
- Knowledge of data security, access controls, and audit logging
Benefits
- Meaningful early-stage equity
- Remote work