AI Agent Builder

Harrison Clarke; Palo Alto (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

Harrison Clarke, a Palo Alto-based startup, is seeking an AI Agent Engineer to build multi-step, tool-using agents that operate across enterprise platforms.

You will design RAG pipelines, memory systems, and robust guardrails while optimizing performance and latency in production clouds. This hands-on role offers real ownership and influence on the core agent framework as the team grows.

Qualifications

• Experience building LLM-powered production systems.
• Strong Python and cloud deployment skills.
• Familiarity with RAG, embeddings, vector databases.
• Experience with multi-step planning and tool-calling.

Responsibilities

• Design and build multi-step, tool-using AI agents.
• Architect RAG pipelines and memory systems.
• Implement planning, execution, and verification loops.
• Develop guardrails and reliability for enterprise environments.
• Optimize inference performance and latency.
• Deploy production systems on cloud infrastructure.

Skills

Python

LLM systems design

Production-grade engineering

Small-team collaboration

AI safety problem solving

Tools

LangGraph

LangChain

AutoGen

Job description

AI Agent Engineer | Palo Alto | Seed-Stage, VC-Backed

We are working with a well-funded, early-stage AI startup based in Palo Alto that is building next-generation enterprise AI agents capable of automating complex, multi-step business workflows.

This is not a chatbot role. The team is focused on designing reliable, production-grade agent systems that can reason, retrieve context, call tools, and execute tasks safely across enterprise platforms such as SAP, Salesforce, and Workday.

Backed by top-tier institutional investors and led by founders with deep AI research and enterprise systems experience, the company is assembling a small, high-caliber engineering team to define what scalable, real-world agent architecture looks like.

The Role

As an AI Agent Engineer, you will:

• Design and build multi-step, tool-using AI agents
• Architect RAG pipelines and memory systems
• Implement planning, execution, and verification loops
• Develop guardrails and reliability mechanisms for enterprise environments
• Optimize inference performance and latency
• Deploy production systems across modern cloud infrastructure

This is a hands-on engineering role with real ownership. Early hires will shape the core agent framework and system design.

What They're Looking For

• Strong experience building LLM-powered systems in production
• Experience with RAG, embeddings, and vector databases
• Familiarity with agent frameworks such as LangGraph, LangChain, AutoGen, or similar
• Experience with tool-calling, API orchestration, and multi-step reasoning workflows
• Strong Python skills and cloud deployment experience
• Comfort operating in small, fast-moving teams

Ideal candidates have built more than just LLM demos. We are looking for engineers who understand how agents fail, how to design around those failures, and how to ship reliable systems.

Why This Opportunity

• Seed-stage but institutionally backed
• Real ownership and architectural influence
• Focused on meaningful enterprise automation problems
• Bay Area based