# AI Field Engineer, EMEA

**Company:** [Fireworks AI](https://hotfix.jobs/companies/fireworks-ai)
**Location:** London, United Kingdom
**Role:** Solutions Architecture
**Experience:** 5+ years
**Skills:** Python, Kubernetes, AWS, Azure, GCP, vLLM, Sglang, Tensorrt-Llm, Gpu Infrastructure, Sft, Dpo, Rft, Llm Inference, Model Serving, Fine-Tuning
**Posted:** 2026-07-22

> Embeds with enterprise customers to build, deploy, and optimize production GenAI systems, from POCs through scaled inference platforms. The role requires at least five years of hands-on customer-facing engineering experience, strong Python and cloud infrastructure skills, and expertise in model serving and fine-tuning.

## Job Description

## Responsibilities

### 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 GenAI products and size deployments for market-scale growth.
- Run load tests and establish latency, throughput, and cost baselines using realistic customer traffic profiles.
- Tune deployments to meet performance targets.
- Deploy and validate model families on inference frameworks, determining optimal model shapes, quantization configurations, and serving patterns.

### Model Strategy and Fine-Tuning
- Guide customers on model selection, fine-tuning strategy, and evaluation methodology.
- Build and run fine-tuning pipelines with customers, balancing model family, compute cost, and quality targets.
- Design and implement evaluation frameworks measuring production-quality metrics.

### Customer Engagement and Stakeholder Management
- Help customers integrate frontier model capabilities into core offerings.
- Lead discovery conversations to understand pain points, constraints, and success criteria.
- Own technical relationships from initial engagement through production deployment.
- Build trust with ML engineers and executives.
- Work on-site with customers and embed with their teams.

### Product Feedback and Platform Improvement
- Translate recurring customer pain points into product proposals and work with engineering and product teams to ship fixes and features.
- Codify repeatable deployment patterns in internal tooling, documentation, and the platform.
- Feed deployment patterns, failure modes, and feature gaps into the product roadmap.

## Requirements

### Minimum Qualifications
- 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.
- Experience building production software with customers and shipping code in another organization’s production environment.
- Strong Python skills, including reading, writing, and debugging production code.
- Familiarity with Kubernetes and infrastructure engineering.
- Working knowledge of the LLM stack, including inference trade-offs, model serving, and fine-tuning workflows; SFT required, with DPO/RFT preferred.
- Experience with cloud infrastructure such as AWS, Azure, or GCP and deploying models on GPU infrastructure.
- Exceptional communication skills across executive, customer, and engineering audiences.

### Nice-to-Haves
- 10+ years in technical field or engineering roles.
- Experience with vLLM, SGLang, TensorRT-LLM, and tuning inference deployments for real workloads.
- Experience building within customer infrastructure and shipping production systems under customer constraints.
- Track record taking GenAI POCs from prototype to production-scale deployments.
- Experience with Azure AI Foundry, AWS Bedrock, AWS SageMaker, or GCP Vertex AI.
- Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.

## Compensation and Benefits

- Work on challenging AI infrastructure problems, including low-latency inference and scalable model serving.
- Build technology that influences how businesses and developers use AI globally.
- High ownership and direct impact in a fast-growing team.
- Collaborate with engineers and AI researchers on innovative technologies.

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