AI Field Engineer – Enterprise (Remote U.S.)

• AI Field Engineer – Enterprise
• Full-Time | Remote (U.S.)
• Compensation: $176,000 – $224,000 Base
• On-Target Earnings (OTE): $220,000 – $280,000 + Meaningful Equity

About the Role

We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with ambitious enterprise customers and turn complex GenAI challenges into production systems—fast.

You'll combine deep hands-on engineering with the executive presence to earn trust across large organizations and help drive engagements from initial technical discovery through production deployment.

What You'll Do

Lead technical discovery calls, scope proof-of-concepts, and run load tests and evaluations to validate the right model architecture and deployment configuration for enterprise customers.

Build end-to-end POCs and production integrations directly inside customer environments while navigating infrastructure, security requirements, and organizational constraints.

Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation frameworks to move from experimentation to production at scale.

Manage relationships across multiple enterprise stakeholders, identifying technical champions and helping align teams to move projects forward efficiently.

Provide recurring customer feedback and deployment insights to the engineering organization to help shape future product development.

Required Qualifications

Deep hands-on experience with LLM inference and/or training.

Working knowledge of open-model frameworks such as:

• vLLM
• SGLang
• TensorRT-LLM

Experience with fine-tuning workflows:

• SFT required
• DPO and/or RFT strongly preferred
• Candidates whose experience is limited to closed-model API integrations alone will not be a fit.
• Proven ability to build and deploy production code within customer environments, including POCs or MVPs running in production.
• Strong Python programming skills.
• Experience with GPU infrastructure.
• Experience using AWS, Azure, or GCP.
• Experience with Kubernetes.
• Strong communication skills with the ability to engage both technical and executive audiences.

Customer-facing engineering experience in roles such as:

Field Engineer

Applied AI Engineer

Solutions Architect

• AI Infrastructure Engineer
• ML Engineer
• Software Engineer with pre-sales exposure
• 3+ years of relevant experience.

Preferred Qualifications

• Experience with DPO or reinforcement fine-tuning (RFT).
• Experience deploying enterprise-scale GenAI applications.
• Experience working directly with production AI infrastructure.
• Interest in contributing to product direction based on customer feedback.
• Comfortable working in fast-paced, high-growth environments with significant ownership.
• Technical Environment
• Python vLLM
• SGLang
• TensorRT-LLM
• Kubernetes
• AWS
• Azure
• GCP
• Azure AI Foundry
• AWS Bedrock
• AWS SageMaker
• GCP Vertex AI
• GPU Infrastructure
• Open-source LLM Frameworks
• LLM Fine-Tuning (SFT, DPO, RFT)

Compensation & Benefits

• Base Salary: $176,000 – $224,000
• On-Target Earnings: $220,000 – $280,000 (80/20 base/variable split)
• Quarterly variable compensation based on individual and team performance
• Compensation may exceed the posted range for candidates with extensive experience.
• Meaningful equity package.

Visa Sponsorship

Visa sponsorship is available.

H-1B transfers TN visas O-1 visas considered on a case-by-case basis Location Remote (United States) Employees located near company hubs may follow a hybrid schedule. Regular travel to enterprise customer sites is expected.

About the Company

This fast-growing AI infrastructure company helps organizations build, fine-tune, and scale production AI applications using open-source models. Its platform is designed to deliver high-performance inference, lower latency, and greater scalability for enterprise AI workloads.

Founded in 2021 , the company has grown to approximately 211 employees and has raised over $327 million in funding. The engineering culture emphasizes technical excellence, ownership, rapid execution, and close collaboration between customer-facing teams and product engineering.

The organization values engineers who enjoy solving complex technical challenges, working directly with customers, and helping shape the future of AI infrastructure.

Originally posted on Himalayas