AI Field Engineer, Enterprise

About the role

Fireworks is an AI platform that lets companies build, train and serve models tailored to their own data and workflows. As an AI Field Engineer in the Enterprise track, you embed with large organizations and digital-native companies adopting GenAI, turning complex AI problems into production systems. You work at the intersection of engineering, product and customer delivery: building POCs, MVPs and production integrations while also leading discovery conversations and executive-level discussions.

Key responsibilities

• Build end-to-end POCs and MVPs alongside customer engineering teams, inside their codebases and infrastructure
• Architect inference foundations and size deployments for GenAI-first customers so infrastructure does not become a bottleneck
• Run load tests, set latency, throughput and cost baselines, and tune deployments
• Deploy and validate new model families on inference frameworks such as vLLM and SGLang, choosing shapes, quantization and serving patterns
• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT) and evaluation methodology, and build fine-tuning pipelines with them
• Design evaluation frameworks that measure production-quality metrics, not just benchmark scores
• Lead discovery conversations, own the technical relationship from first contact through production deployment, and spend time on-site with customers
• Turn customer pain points into product proposals and feed deployment patterns and failure modes back into the platform and roadmap

Required profile

• 5+ years in a hands-on, customer-facing technical role such as Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder
• Proven experience shipping production code in another company's environment
• Exceptional communication: able to run discovery calls, present to a VP and debug issues with an ML engineer in the same day
• Preferred: 10+ years in technical roles, a track record moving GenAI POCs to production, and experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)

Required skills

• Strong Python, including reading, writing and debugging production code
• Familiarity with Kubernetes and infrastructure engineering
• Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum)
• Cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure
• Preferred: inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and agentic systems or AI-native developer toolchains