AI Field Engineer - Enterprise

We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with Fireworks AI's most ambitious enterprise customers and turn complex GenAI challenges into production systems — fast. You'll be the technical tip of the spear, pairing deep hands-on engineering with the executive presence to earn trust across large organizations and drive deals from first discovery call to production deployment.

What will you be doing?

Lead technical discovery calls, scope POCs, and run load tests and evaluations to validate the right model architecture and deployment configuration for each enterprise customer

Build end-to-end POCs and production integrations hands-on-keyboard inside customer environments, navigating their infrastructure, security requirements, and organizational constraints

Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation frameworks — moving them from open-model exploration to production at scale

Manage multi-stakeholder enterprise relationships — identifying technical champions, navigating org politics, and aligning the right people to move deals forward quickly

Feed recurring customer pain points and deployment patterns back into the product roadmap, acting as a direct feedback loop between the field and engineering

Key Requirements

Deep hands-on experience with LLM inference and/or training — working knowledge of open-model frameworks (vLLM, SGLang, TensorRT-LLM) and fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus); candidates with only closed-model/API-wrapper experience will not clear the bar

Proven ability to ship production code inside a customer's environment — not just advisory work; you've built and deployed POCs/MVPs that ran in someone else's prod system

Strong Python skills plus GPU/cloud infrastructure experience (AWS, Azure, or GCP) and comfort with Kubernetes

Executive presence and enterprise navigation skills — able to run a technical deep-dive with an ML engineer and present architecture trade-offs to a VP in the same afternoon

Pre-sales or customer-facing field engineering experience (FDE, Applied AI Engineer, Solutions Architect, or similar); pure software engineers without customer-facing exposure are not a fit