Forward Deployed Engineer (Generative AI)

Forward Deployed Engineer - Generative AI

Location: Remote (US or Canada-based)

Salary: $175K-$225K base (OTE: $220K-$280K; variable paid quarterly) + meaningful equity.

Sponsorship: H-1B transfers and TN visas sponsored. O-1 considered case by case.

I’m recruiting on behalf of a confidential client, a well-funded, 200+ person AI inference platform that helps enterprises build, tune and scale AI on open models. You’ll embed with their 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 you’ll be doing:

• Leading technical discovery calls, scoping POCs, and running load tests and evaluations to validate the right model architecture and deployment configuration for each customer
• Building end-to-end POCs and production integrations hands-on-keyboard inside customer environments, navigating their infrastructure, security requirements and organizational constraints
• Guiding customers on model selection, fine-tuning strategy (SFT, DPO, RFT) and evaluation frameworks, moving them from open-model exploration to production at scale
• Managing multi-stakeholder enterprise relationships: identifying technical champions, navigating org politics and aligning the right people to move deals forward
• Feeding recurring customer pain points and deployment patterns back into the product roadmap

You’ll likely be a fit if you have:

• 3+ years of experience in customer-facing AI/ML field engineering (FDE, Applied AI, Solutions Architect, AI Infra, or a software or ML engineer with pre-sales exposure)
• Deep hands-on experience with LLM inference and/or training, including open-model frameworks (vLLM, SGLang, TensorRT-LLM) and fine-tuning workflows (SFT at minimum, DPO/RFT a strong plus)
• A track record of shipping production code inside a customer’s environment, with POCs or MVPs that ran in someone else’s prod system
• Strong Python, GPU/cloud infrastructure experience (AWS, Azure or GCP) and comfort with Kubernetes
• Executive presence: able to run a technical deep-dive with an ML engineer and present architecture trade-offs to a VP in the same afternoon

What you won’t find here:

This role won’t suit you if your experience is only closed-model or API-wrapper work, or if you’re a pure software engineer without customer-facing exposure.

Skills:

• Python
• vLLM
• SGLang
• TensorRT-LLM
• Kubernetes
• AWS, Azure, GCP
• LLM Fine-Tuning (SFT, DPO, RFT)
• GPU Infrastructure