AI Field Engineer - Enterprise

About the Company

Our client is a Series D AI inference platform for building, tuning and scaling AI on open models. Founded in 2021, it has raised about $1.5B, has crossed $1 billion in annualized revenue and powers production workloads at some of the best-known technology companies. The team is roughly 200 people.

About the Role

Embed with the company's most ambitious enterprise customers and turn complex GenAI challenges into production systems. You are 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.

What You Will Do

• Lead technical discovery calls, scope proofs of concept and run load tests and evaluations to choose the right model architecture and deployment configuration.
• Build end-to-end POCs and production integrations hands-on inside customer environments, working within their infrastructure and security constraints.
• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT) and evaluation frameworks.
• Manage multi-stakeholder enterprise relationships and move deals forward quickly.
• Feed recurring customer pain points back into the product roadmap.

Requirements

What We Are Looking For

• Three or more years in client-facing AI or ML roles such as solutions architect, forward-deployed engineer, applied AI engineer or sales engineer.
• Hands-on LLM inference and fine-tuning experience with open-model frameworks such as vLLM, SGLang or TensorRT-LLM. Closed-model API work alone is not enough.
• Production code shipped inside a customer's environment, not just advisory work.
• Strong Python, GPU and cloud infrastructure experience (AWS, Azure or GCP) and comfort with Kubernetes.
• Executive presence: you can run a technical deep-dive with an ML engineer and present trade-offs to a VP in the same afternoon.
• Pre-sales ownership from discovery through POC scoping, evaluations and model selection.