AI Field Engineer - Strategic Partnerships

The Role

As an AI Field Engineer for Strategic Partnerships, you will be one of the technical owners of Fireworks' most strategic partnership. You’ll work closely with Strategic Partner field teams, Partner-aligned ISVs, and the SIs that run enterprise AI transformation programs to make Fireworks the default inference and fine-tuning layer in every Partner AI architecture. The role sits at the intersection of engineering, partner development, and customer delivery. You build reference architectures, run benchmarks, debug production integrations, and co-develop POCs — all while holding your own in executive-level conversations about strategy, roadmap, and business outcomes.

You spend most of your time building and enabling. You ship code, run joint POCs with Partner field teams, and architect deployments that span Strategic Partners and Fireworks. But you also lead discovery conversations, align partner stakeholders, and translate field signals into product improvements that compress the feedback loop from partner to roadmap.

Minimum Qualifications

• 3+ years in a pre-sales, partner engineering, forward-deployed, or technical consulting role.

• Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment.

• Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering.

• Hands-on fluency with LLM inference: latency/throughput tradeoffs, batching strategies, quantization, structured outputs, function calling. You can explain why 50ms p99 matters to an enterprise CTO.

• Real experience with fine-tuning — LoRA at minimum, RFT a strong plus. You understand when SFT is enough and when it isn't.

• Deep familiarity with the Azure AI stack: Azure Foundry, Azure OpenAI Service, Azure ML, AKS, Entra/RBAC for AI workloads. You know where Fireworks fits and where it doesn't.

• Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon.

Preferred Qualifications

• 5+ years in technical field or engineering roles where you've owned a technical relationship with a hyperscaler or major SI, not just supported one

• Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads.

• Prior role at a hyperscaler, AI-native cloud, or inference provider.

• Deep familiarity with other strategic partner stacks: Platforms for AI workloads, network, and identity integration patterns. You know where Fireworks fits and where it doesn't.

• Experience with agentic frameworks (LangChain, LlamaIndex, or custom tool-use pipelines) — you understand how inference latency and reliability shapes agent behavior at scale.

• Background in model evaluation — you understand why benchmark gaming is rampant and what rigorous evals actually look like.

• You've written a technical blog post or reference architecture that people actually read.

• Track record taking GenAI POCs from prototype to production-scale deployments.