Forward Deployed AI Engineer (Agent Engineering)

SAP•Stanford, CA

4d•$106,900 - $229,400•Hybrid

About The Position

We are building a new model for enterprise software: working directly with customers to design, build, and deploy AI agents that automate real workflows and decision-making in production within weeks—not quarters. As a Forward Deployed AI Engineer, you will partner directly with customers and product managers to design, build, and deploy production-grade AI agent systems that operate at scale—handling real business processes and high-volume interactions. You will own the end-to-end implementation of these systems, from architecture and integrations to deployment and iteration in live environments. This is a high-ownership, fast-paced role at the intersection of engineering and customer delivery. You are expected to move quickly from concept to production, building full-stack solutions and agentic systems that are reliable, scalable, and continuously improving. This is a deeply hands-on role. You are expected to design and build complete systems, create tooling and harnesses to accelerate development, and use modern AI tools to prototype, debug, and iterate rapidly—not just contribute isolated components.

Requirements

3-5+ years in software engineering (full-stack or backend)
Experience building API-driven systems and integrations
Familiarity with LLMs or AI-based systems

Nice To Haves

Experience with agent systems, orchestration frameworks, or AI tooling

Responsibilities

Build and deploy agentic systems for enterprise workflows: Design and implement AI agents (and multi-agent systems) that reason and retrieve data across complex business processes and take action in enterprise systems.
Collaborate directly with customers and cross-functional teams: Work closely with customer stakeholders to refine solutions, test prototypes, and ensure they meet real-world needs.
Develop agentic harnesses to accelerate development: Create evaluation frameworks, toolchains, and workflows that enable rapid iteration and improve system reliability.
Own full-stack implementation and integrations: Build across LLMs, APIs, backend systems, and lightweight UIs to deliver complete, working solutions.
Design orchestration, reasoning, and workflows: Architect how agents plan, use tools, and coordinate across complex, multi-step processes.
Ensure reliability, safety, and production readiness: Implement guardrails, validation logic, and fallback mechanisms to ensure consistent and trustworthy behavior in production.