AI Field Engineer - Enterprise

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.