# AI Field Engineer

**Company:** [Fireworks AI](https://hotfix.jobs/companies/fireworks-ai)
**Location:** San Mateo, CA
**Role:** Solutions Architecture
**Salary:** $280k – $320k/yr
**Experience:** 3+ years
**Skills:** Python, Kubernetes, vLLM, Sglang, Tensorrt-Llm, Azure Ai, Azure Openai Service, Azure Ml, Aks, Lora, LangChain, Llamaindex
**Posted:** 2026-06-11

> Technical lead for Microsoft co-sell motions, building POCs, deploying LLMs on vLLM/SGLang, guiding fine-tuning strategy, and owning partner feedback loops. Requires 3+ years in pre-sales or partner engineering with strong Python, LLM inference, and Azure AI experience.

## Job Description

## Technical Delivery and Deployment
- Be the technical lead on co-sell motions with Microsoft — joint reference architectures, Azure Foundry integration patterns, and shared POCs for strategic accounts.
- Build end-to-end POCs and MVPs alongside partner engineering teams, working inside their codebases, infrastructure, and constraints.
- Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets.
- Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads.

## Model Strategy and Fine-Tuning
- Guide Microsoft’s customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
- Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets.
- Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores.

## Product Feedback and Platform Improvement
- Own the feedback loop — surface partner-driven product gaps to Fireworks engineering, and translate the roadmap back into partner messaging.
- Ship external technical content: reference architectures, integration guides, and benchmark posts that make it easy for partners to win deals with us.
- Track pipeline health; flag risks and opportunities to Field leadership weekly.

## 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.
- Real experience with fine-tuning — LoRA at minimum, RFT a strong plus.
- Deep familiarity with the Azure AI stack: Azure Foundry, Azure OpenAI Service, Azure ML, AKS, Entra/RBAC for AI workloads.
- 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.
- 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.
- Experience with agentic frameworks (LangChain, LlamaIndex, or custom tool-use pipelines).
- Background in model evaluation.
- Written a technical blog post or reference architecture that people actually read.
- Track record taking GenAI POCs from prototype to production-scale deployments.

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**Apply:** https://hotfix.jobs/jobs/3774baee-b881-4300-bd2a-7c877c66b0e1
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