Agentic AI Product Engineer

Simplex•Austin, TX

About The Position

An Austin-based SaaS company is hiring its first Agentic AI Product Engineer to help build a next-generation platform that turns a system of record into a system of intelligence. The engineer will own the Agentic AI roadmap and deliver the first roster of agents for launch. This is a hands-on role encompassing the agent-building harness and the production improvement loop. The engineer will establish the toolkit, then build, run, and maintain agents. Responsibilities span infrastructure, agent design and implementation, and monitoring and evaluation after deployment. The company is seeking someone deeply engaged with LLMs who has shipped agents used by customers. The company offers a generous salary and equity. It cannot provide sponsorship or engage candidates on a C2C basis.

Requirements

Deep, demonstrated knowledge of Agentic AI.
A track record of shipping agents in SaaS that run in production for paying customers.
Hands-on experience with tooling for building, monitoring, observing, evaluating, metering, and costing LLM systems.
Engineering experience with Python or TypeScript, APIs, event-driven services, and cloud infrastructure.
Ability to turn messy workflows into agent specifications with clear success metrics.
A continuous-improvement mindset toward production agents.
Strong communication skills and ability to work as part of a team.

Nice To Haves

Experience with multi-tenant SaaS data and the security and isolation considerations involved in giving agents access to it.
Experience with document-heavy workflows, including extraction, classification, and comparison of PDFs, drawings, and spreadsheets.
Experience with deep-search or intelligence agents.
A degree, bootcamp, or vocational AI program is welcome, though demonstrated work and achievements are emphasized.

Responsibilities

Own the Agentic AI roadmap and deliver the initial roster of agents for launch.
Build and own the harness used to develop agents and the continuous-improvement production loop.
Set up the toolkit and build, run, and maintain agents.
Own agent infrastructure, design, and implementation.
Monitor and evaluate agents after deployment.
Translate messy product workflows into agent specifications with clear success metrics.