# AI Field Engineer - Enterprise

**Company:** [Fireworks AI](https://hotfix.jobs/companies/fireworks-ai)
**Location:** New York, NY, San Mateo, CA
**Role:** Solutions Architecture
**Salary:** $200k – $260k/yr
**Experience:** 5+ years
**Skills:** Python, Kubernetes, vLLM, Sglang, Tensorrt-Llm, AWS, Azure, GCP, Fine-Tuning, Llm Inference
**Posted:** 2026-06-11

> Hands-on AI Field Engineer embedding with enterprise customers to build and deploy production GenAI systems, run fine-tuning pipelines, and manage technical relationships from POC through scale.

## Job Description

## Technical Delivery and Deployment
- Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.
- Architect inference foundations for customers whose core product is built on GenAI; size deployments to scale without infrastructure bottlenecks.
- Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles; tune deployments to hit targets.
- Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns.

## Model Strategy and Fine-Tuning
- Guide 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.

## Customer Engagement and Stakeholder Management
- Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria.
- Own the technical relationship from first engagement through production deployment.
- Spend time on-site with customers, embedding with their teams.

## Product Feedback and Platform Improvement
- Identify recurring customer pain points and translate them into concrete product proposals.
- Codify repeatable deployment patterns and contribute them back to internal tooling and documentation.
- Feed customer signals back into the product roadmap.

## Minimum Qualifications
- 5+ years in a hands-on, customer-facing technical role (Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder).
- Demonstrated ability to build production software with customers and ship code running in someone else's production environment.
- Strong Python skills; familiarity with Kubernetes and infrastructure engineering.
- Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus).
- Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.
- Exceptional communication skills.

## Preferred Qualifications
- 10+ years in technical field or engineering roles.
- Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads.
- Experience operating as a technical authority inside a customer's environment.
- 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.

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**Apply:** https://hotfix.jobs/jobs/c0a611e7-5691-44c3-b83e-3b053bf3e765
**Canonical:** https://hotfix.jobs/jobs/c0a611e7-5691-44c3-b83e-3b053bf3e765