AI Field Engineer - Enterprise

Job Description

Job Description: AI Field Engineer (Enterprise Track)

Location: New York, NY

About The Role

AI Field Engineers at Fireworks embed with ambitious customers and technology partners to solve complex AI problems and build production systems. The role combines hands-on engineering (building POCs, MVPs, production integrations) with executive-level conversations about architecture, strategy, and business outcomes.

Key Responsibilities

• Technical Delivery & Deployment: Build end-to-end POCs and MVPs; architect inference foundations; run load tests and tune deployments; deploy models on inference frameworks (vLLM, SGLang).
• Model Strategy & Fine-Tuning: Guide customers on model selection and fine-tuning strategy (SFT, DPO, RFT); build and run fine-tuning pipelines; design evaluation frameworks for production-quality metrics.
• Customer Engagement & Stakeholder Management: Lead discovery conversations; own technical relationships from first contact to production; spend time on-site with customers.
• Product Feedback & Platform Improvement: Identify customer pain points; translate into product proposals; feed deployment patterns and failure modes back into the product roadmap.
  
  

Minimum Qualifications

• 5 years in a hands-on, customer-facing technical role (e.g., Forward Deployed Engineer, Solutions Architect, ML Engineer with field exposure).
• Proven ability to build production software with customers, not just advise.
• Strong Python skills; comfortable reading, writing, and debugging production code.
• Working knowledge of LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum).
• Experience with cloud infrastructure (AWS, Azure, GCP) and GPU deployments.
• Exceptional communication skills for discovery calls, executive presentations, and debugging with engineers.
  
  

Preferred Qualifications

• 10 years in technical field or engineering roles.
• Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments.
• Experience operating as a technical authority inside customer environments.
• Track record taking GenAI POCs from prototype to production-scale deployments.
• Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex).
• Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.