# AI Field Engineer

**Company:** [Fireworks AI](https://hotfix.jobs/companies/fireworks-ai)
**Location:** London, United Kingdom
**Role:** Solutions Architecture
**Salary:** SGD200k – SGD350k/yr
**Experience:** 5+ years
**Skills:** Python, Kubernetes, AWS, Azure, GCP, Gpu Infrastructure, vLLM, Sglang, Tensorrt-Llm, Sft, Dpo, Rft, Llm Inference, Fine-Tuning, Agentic Systems
**Posted:** 2026-07-23

> Build and deploy production GenAI systems with enterprise customers, from discovery and proof of concept through scaled inference deployments. The role requires strong Python, cloud, Kubernetes, LLM serving, fine-tuning, evaluation, and executive-level communication skills.

## Job Description

## Responsibilities

### Technical Delivery and Deployment
- Build end-to-end POCs and MVPs alongside customer engineering teams within their codebases, infrastructure, and constraints.
- Architect inference foundations for customers whose core products depend on GenAI, and size deployments for market-scale growth.
- Run load tests and establish latency, throughput, and cost baselines using realistic traffic profiles; tune deployments to meet targets.
- Deploy and validate model families on inference frameworks such as vLLM and SGLang, 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, balancing model families, compute cost, and quality targets.
- Design and implement evaluation frameworks that measure production-quality metrics.

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

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

## Requirements
- 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 and technical audiences.

## Nice-to-Haves
- 10+ years in technical field or engineering roles.
- Experience with inference serving frameworks including vLLM, SGLang, and TensorRT-LLM.
- Experience operating as a technical authority within customer environments and shipping production systems within their infrastructure.
- Track record taking GenAI POCs from prototype to production-scale deployment.
- Experience with Azure AI Foundry, AWS Bedrock, AWS SageMaker, or GCP Vertex.
- Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.

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**Canonical:** https://hotfix.jobs/jobs/d8721d88-570a-4e93-8cac-83abb7e1c135