AI Field Engineer – Enterprise

# AI Field Engineer – Enterprise ## About the Role We're looking for an AI Field Engineer – Enterprise to help enterprise customers bring advanced AI models into production. This role combines hands-on AI engineering, technical problem-solving, and customer collaboration. You'll work closely with enterprise customers and internal engineering and sales teams to understand complex requirements, build proof-of-concepts, optimize model inference, and deliver reliable AI solutions in real-world production environments. This is an opportunity for an engineer who enjoys working at the intersection of open-source LLMs, AI infrastructure, and enterprise applications. ## What You'll Do - Partner with enterprise customers to understand technical requirements, identify use cases, and design AI implementation strategies. - Build and deploy proof-of-concepts and production integrations directly within customer environments. - Implement and optimize LLM inference and serving workflows using technologies such as vLLM, SGLang, and TensorRT-LLM. - Work with open-source language models, fine-tuning workflows, and model evaluation techniques. - Diagnose performance bottlenecks and improve inference latency, throughput, GPU utilization, and deployment efficiency. - Develop production-quality Python code, APIs, integrations, and technical prototypes. - Collaborate with sales teams on technical discovery, solution design, and enterprise deal strategy. - Present technical solutions and architecture recommendations to engineers, technical leaders, and executive stakeholders. - Translate customer feedback into actionable insights for internal product and engineering teams. - Support customers throughout technical validation, deployment, and production adoption. ## What We're Looking For - 3+ years of relevant experience in AI/ML engineering, applied AI, solutions engineering, field engineering, or a related technical role. - Strong Python programming and software engineering fundamentals. - Hands-on experience building and deploying LLM-powered applications or AI systems in production. - Experience with open-source LLM inference or serving frameworks such as vLLM, SGLang, or TensorRT-LLM. - Familiarity with model fine-tuning approaches such as supervised fine-tuning (SFT); experience with DPO or reinforcement-based fine-tuning is a plus. - Understanding of GPU-based workloads, inference performance, and cloud infrastructure. - Experience with Docker, Kubernetes, and production deployment workflows. - Ability to build technical POCs and take ownership of implementations from discovery through deployment. - Strong communication skills and the ability to work directly with enterprise customers. - Comfort collaborating with sales teams and translating technical capabilities into business value. ## Preferred Qualifications - Experience as a Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, or Solutions Architect. - Experience deploying open-source models in enterprise environments. - Familiarity with model evaluation, inference optimization, distributed serving, and GPU infrastructure. - Experience working with enterprise technical stakeholders and complex customer requirements. ## Location and Work Model US-based opportunity with office hubs in San Mateo, California, and New York City. The role is remote-friendly, with a hybrid schedule for employees near an office hub. Regular travel to enterprise customer locations is expected. Candidates should be comfortable working directly with customers and supporting enterprise deployments. ## Compensation - Base salary: $176,000–$224,000 per year. - Target on-target earnings (OTE): $220,000–$280,000 per year. - Equity: Available as part of the compensation package. Final compensation depends on experience, qualifications, location, and the overall offer structure. ## Visa Sponsorship H-1B transfers and TN visas are supported. O-1 sponsorship is considered on a case-by-case basis. Individual eligibility and sponsorship requirements will be evaluated during the hiring process. ## Interview Process The interview process may include an initial recruiter screen, a take-home exercise, a technical/culture interview, a discovery and hiring-manager discussion, an onsite final round, an executive interview, and a debrief. ## Interested? If you enjoy solving challenging AI infrastructure problems, building production-grade LLM applications, and working closely with enterprise customers, we'd like to hear from you. Please share your resume and relevant examples of production AI deployments, open-source LLM serving, fine-tuning, or customer-facing engineering projects.