Forward Deployed Engineer (Generative AI)

Confidential; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Confidential, a well-funded AI inference platform, is seeking a senior customer-facing field engineer to join remotely from the US or Canada. You will engage with enterprise clients to scope POCs, validate model architectures, and drive production deployments from discovery to rollout.

You will combine hands-on engineering with executive-level communication, guiding customers on model selection and fine-tuning strategies while influencing the product roadmap based on deployment patterns and pain

Qualifications

• 3+ years in customer-facing AI/ML field engineering (FDE, Applied AI, Solutions Architect, AI Infra, or related roles).
• Hands-on experience with LLM inference/training and open-model frameworks (vLLM, SGLang, TensorRT-LLM).
• Proven track record shipping production code inside a customer's prod system with POCs/MVPs.
• Strong Python and GPU/cloud infrastructure experience (AWS, Azure or GCP) and proficiency with Kubernetes.
• Ability to present architecture trade-offs to executives and technical teams alike.

Responsibilities

• Lead technical discovery calls, scope POCs, and run load tests to validate model architecture and deployment.
• Build end-to-end POCs and production integrations inside customer environments.
• Guide customers on model selection and fine-tuning strategies (SFT, DPO, RFT) for production at scale.
• Manage multi-stakeholder enterprise relationships and align teams to move deals forward.
• Feed customer pain points back into the product roadmap.

Skills

Customer-facing AI/ML

Python

Executive presence

Pre-sales exposure

Tools

AWS

Azure

GCP

Kubernetes

vLLM

SGLang

TensorRT-LLM

Job description

Location: Remote (US or Canada-based)

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.