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# Software Engineer, Forward Deployed Agent Builder

- **Company:** Brex Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $152,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Application Programming Interfaces (APIs), Artificial Intelligence, NoSQL, Sql Optimization, Large Language Models, Multi-Agent Systems
- **Published:** August 14, 2026
- **Apply:** https://www.dice.com/job-detail/9a81a0d4-3959-4a52-86d9-14b4bb3ef4f5

## About the Role

* 4+ years in engineering, technical product/program management, applied AI, or a similar technical role, with a track record of shipping AI/automation systems to production.
 * Hands-on experience building with LLMs, agent frameworks, and tool-use patterns (e.g., MCP, function calling, RAG) across the full stack (data, APIs, orchestration, product).
 * Experience designing and optimizing SQL and/or NoSQL databases, including data modeling, query performance tuning, and schema design.
 * Strong ability to decompose human workflows into scalable, agentic systems, grounded in a deep curiosity for how people work.
 * High ownership and cross-functional influence, with the ability to quickly build trust across teams and seniority levels.

Bonus points

 * Experience automating back-office functions rather than just customer-facing AI features.
 * Experience with OpenClaw, Hermes, or other general-purpose agent harnesses.

## Description

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday, and Thursday. As a perk, we also have up to four weeks per year of fully remote work!, * Embed with partner teams to deeply understand workflows, including shadowing and learning different job functions.

 * Scope, design, and deploy agents that take over real workflows across internal orgs.
 * Integrate agents with internal systems, APIs, and data sources.
 * Define evaluation frameworks, success metrics, and feedback loops to measure and improve agent performance.
 * Build shared tooling and contribute to playbooks that accelerate deployments, while owning reliability, adoption, and business impact.


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