AI Field Engineer

AI Field Engineer (Enterprise)

Medilinkers, in partnership with AI Talent Hunt, is conducting a confidential search on behalf of a rapidly growing AI infrastructure company.

About the Role:

Medilinkers, in partnership with AI Talent Hunt, is looking for an AI Field Engineer (Enterprise) with 3 years of experience to embed with some of the most ambitious enterprise customers and turn complex GenAI challenges into production systems — fast. You'll combine deep hands-on engineering expertise with strong customer-facing skills to drive technical evaluations, proof-of-concepts, and production deployments across enterprise environments.

What You'll 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 within customer environments, navigating infrastructure, security requirements, and organizational constraints.
• Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation frameworks, helping them move from experimentation to production-scale deployments.
• Manage multi-stakeholder enterprise relationships, identify technical champions, navigate organizational dynamics, and align stakeholders to accelerate successful outcomes.
• Provide feedback on recurring customer pain points and deployment patterns, serving as a bridge between customers and engineering teams.

Key Requirements

• Deep hands-on experience with LLM inference and/or training, including open-model frameworks such as vLLM, SGLang, and TensorRT-LLM.
• Experience with fine-tuning workflows (SFT required; DPO/RFT strongly preferred).
• Proven track record of building and deploying production solutions within customer environments.
• Strong Python programming skills and experience with cloud platforms (AWS, Azure, or GCP).
• Comfortable working with Kubernetes and GPU-based infrastructure.
• Strong executive presence with the ability to communicate effectively with both technical and business stakeholders.
• Previous experience in customer-facing technical roles such as Field Deployment Engineer, Applied AI Engineer, Solutions Architect, or similar.

Compensation

• Base Salary: $176,000 – $224,000
• On-Target Earnings (OTE): $220,000 – $280,000
• Quarterly performance-based variable compensation
• Competitive equity package
• Additional compensation available for highly experienced candidates

Visa Sponsorship

• H-1B Transfers Supported
• TN Visa Sponsorship Available
• O-1 Visa Considered on a Case-by-Case Basis

Work Arrangement

• Full-time position
• US-based, remote-friendly
• Regular travel to enterprise customer sites required
• Hybrid work arrangement available for candidates located near company hubs

Preferred Locations

• San Mateo, California
• New York, New York

Tech Stack

Python, vLLM, SGLang, TensorRT-LLM, Kubernetes, AWS, Azure, GCP, Azure AI Foundry, AWS Bedrock, AWS SageMaker, GCP Vertex AI, LLM Fine-Tuning (SFT, DPO, RFT), GPU Infrastructure, and Open-Source LLM Frameworks.

Ideal Candidate Profile

Seniority

• 3 years of experience in customer-facing AI/ML field engineering roles such as Field Deployment Engineer (FDE), Applied AI Engineer, Solutions Architect, AI Infrastructure Engineer, ML Engineer, Software Engineer with pre-sales exposure, or research professionals transitioning into customer-facing roles.

Work Experience

• Successfully shipped AI/ML production code within customer environments.
• Hands-on experience with LLM inference and fine-tuning, including running SFT pipelines, benchmarking latency, and optimizing open-model deployments.
• Experience managing the full customer engagement lifecycle, including discovery, POC scoping, load testing, evaluations, and model selection.
• Background working at AI-native, AI infrastructure, MLOps, developer tooling, or enterprise SaaS companies with AI-powered products.

Technical Skills

• Experience with LLM serving frameworks such as vLLM, SGLang, and TensorRT-LLM.
• Strong Python and Kubernetes skills.
• Practical experience training and fine-tuning open models using methodologies such as SFT, DPO, and RFT.
• Knowledge of GPU optimization and performance tuning for LLM workloads.

Professional Skills

• Strong executive presence and ability to engage effectively with enterprise stakeholders.
• Experience navigating enterprise environments, including technical champions, security reviews, procurement processes, and cross-functional decision makers.

Additional Requirements

• Willingness to travel domestically to customer locations as needed.

Candidates Who Are Unlikely to Be a Fit

• Professionals whose LLM experience is limited to closed-model API integrations without exposure to open-model inference, serving frameworks, or fine-tuning.
• Advisory or consulting profiles that lack hands-on production deployment experience.
• Candidates from large technology companies who have not operated in startup, high-growth, or customer-facing field engineering environments.