AT
AI Field Engineer - Enterprise
- Salary
- $176K–$224KUSD
- Moves you to
- United States
- Support
- Visa sponsorship
- Posted
- Oct 1, 2026
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We’re Hiring | AI Field Engineer – Enterprise
We’re looking for a highly hands-on AI Field Engineer / Forward Deployed Engineer / Applied AI Engineer / Solutions Architect to help enterprise customers move from GenAI experimentation to production.
Location: San Mateo, CA / New York, NY
Employment: Full-time
Work Mode: Hybrid / US-based remote-friendly
Base Salary: $176K–$224K
OTE: $220K–$280K + Equity
Travel: Regular domestic travel to enterprise customers as needed
What We’re Looking For
• 3+ years of customer-facing AI/ML field engineering experience
• Experience shipping AI/ML production code inside customer environments
• Strong Python and Kubernetes skills
• Hands-on experience with LLM inference and/or training
• Experience with open-model serving frameworks such as vLLM, SGLang, or TensorRT-LLM
• Understanding of LLM inference trade-offs, model selection, agents, latency and performance optimization
• Hands-on fine-tuning experience — SFT required; DPO/RFT strongly preferred
• Experience with GPU optimization and LLM workloads
• Cloud infrastructure experience across AWS, Azure, or GCP
• Experience with platforms such as AWS Bedrock, SageMaker, Azure AI Foundry, or Google Vertex AI is a plus
• Experience with POCs, load testing, evaluations and production deployments
• Strong enterprise customer-facing and pre-sales / field engineering experience
• Ability to communicate technical architecture and trade-offs to both ML engineers and executive stakeholders
What You’ll Do
🔹 Lead technical discovery sessions and scope enterprise POCs
🔹 Build end-to-end POCs and production integrations inside customer environments
🔹 Run load tests, evaluations and deployment benchmarks
🔹 Advise customers on model selection, inference architecture and fine-tuning strategies
🔹 Help customers move open-model experimentation into production at scale
🔹 Navigate enterprise infrastructure, security requirements and procurement processes
🔹 Build relationships with technical champions and multiple enterprise stakeholders
🔹 Partner closely with sales teams to influence technical strategy and drive complex deals
🔹 Identify recurring customer challenges and provide feedback to product and engineering teams
Important
This role requires deep hands-on technical experience.
Candidates whose experience is primarily limited to closed-model API wrappers without exposure to open-model inference, serving frameworks or fine-tuning will not be considered.
Pure advisory/consulting profiles without production engineering experience are also not aligned with this role.
Visa & Work Authorization
Visa sponsorship/transfer support may be available for eligible candidates, including H-1B transfers and TN visas. O-1 may be considered case-by-case.
Interested? DM me your resume or message me directly on LinkedIn.
If you know someone who could be a strong fit, feel free to tag them or share this opportunity.
#Hiring #AIJobs #ArtificialIntelligence #MachineLearning #GenerativeAI #LLM #AIEngineer #FieldEngineer #ForwardDeployedEngineer #SolutionsArchitect #AppliedAI #MLOps #Python #Kubernetes #LLMInference #FineTuning #GPU #CloudComputing #AWS #Azure #GCP #TechJobs #USJobs
We’re looking for a highly hands-on AI Field Engineer / Forward Deployed Engineer / Applied AI Engineer / Solutions Architect to help enterprise customers move from GenAI experimentation to production.
Location: San Mateo, CA / New York, NY
Employment: Full-time
Work Mode: Hybrid / US-based remote-friendly
Base Salary: $176K–$224K
OTE: $220K–$280K + Equity
Travel: Regular domestic travel to enterprise customers as needed
What We’re Looking For
• 3+ years of customer-facing AI/ML field engineering experience
• Experience shipping AI/ML production code inside customer environments
• Strong Python and Kubernetes skills
• Hands-on experience with LLM inference and/or training
• Experience with open-model serving frameworks such as vLLM, SGLang, or TensorRT-LLM
• Understanding of LLM inference trade-offs, model selection, agents, latency and performance optimization
• Hands-on fine-tuning experience — SFT required; DPO/RFT strongly preferred
• Experience with GPU optimization and LLM workloads
• Cloud infrastructure experience across AWS, Azure, or GCP
• Experience with platforms such as AWS Bedrock, SageMaker, Azure AI Foundry, or Google Vertex AI is a plus
• Experience with POCs, load testing, evaluations and production deployments
• Strong enterprise customer-facing and pre-sales / field engineering experience
• Ability to communicate technical architecture and trade-offs to both ML engineers and executive stakeholders
What You’ll Do
🔹 Lead technical discovery sessions and scope enterprise POCs
🔹 Build end-to-end POCs and production integrations inside customer environments
🔹 Run load tests, evaluations and deployment benchmarks
🔹 Advise customers on model selection, inference architecture and fine-tuning strategies
🔹 Help customers move open-model experimentation into production at scale
🔹 Navigate enterprise infrastructure, security requirements and procurement processes
🔹 Build relationships with technical champions and multiple enterprise stakeholders
🔹 Partner closely with sales teams to influence technical strategy and drive complex deals
🔹 Identify recurring customer challenges and provide feedback to product and engineering teams
Important
This role requires deep hands-on technical experience.
Candidates whose experience is primarily limited to closed-model API wrappers without exposure to open-model inference, serving frameworks or fine-tuning will not be considered.
Pure advisory/consulting profiles without production engineering experience are also not aligned with this role.
Visa & Work Authorization
Visa sponsorship/transfer support may be available for eligible candidates, including H-1B transfers and TN visas. O-1 may be considered case-by-case.
Interested? DM me your resume or message me directly on LinkedIn.
If you know someone who could be a strong fit, feel free to tag them or share this opportunity.
#Hiring #AIJobs #ArtificialIntelligence #MachineLearning #GenerativeAI #LLM #AIEngineer #FieldEngineer #ForwardDeployedEngineer #SolutionsArchitect #AppliedAI #MLOps #Python #Kubernetes #LLMInference #FineTuning #GPU #CloudComputing #AWS #Azure #GCP #TechJobs #USJobs