AI Field Engineer, EMEA
Hybrid (London)full-timePosted 2mo ago
About the role
AI Field Engineers embed with customers to turn complex AI problems into production systems, combining hands-on technical delivery with executive-level engagement to design, deploy, and scale GenAI solutions for enterprise customers.
Responsibilities
- Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.
- Architect inference foundations and size deployments for scalable GenAI products.
- Run load tests to establish latency, throughput, and cost baselines and tune deployments to targets.
- Deploy and validate new model families on inference frameworks (vLLM, SGLang), determine optimal shapes, quantization, and serving patterns.
- Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation methodology.
- Build and run fine-tuning pipelines with customers, navigating trade-offs between model families, compute cost, and quality targets.
- Design and implement evaluation frameworks measuring production-quality metrics.
- Lead structured discovery conversations and manage technical relationships from first engagement to production deployment.
- Spend time on-site with customers to build trust and momentum.
- Identify recurring customer pain points and translate them into concrete product proposals for tooling and platform improvements.
- Codify repeatable deployment patterns and contribute to internal tooling and documentation.
- Feed customer signals back into the product roadmap with specificity and urgency.
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).
- Proven ability to build production software with customers and ship code in production environments.
- Strong Python skills; comfortable reading, writing, and debugging production code.
- 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: capable of running discovery calls, presenting to VPs, and debugging latency with ML engineers.
Skills
Required:PythonKubernetesLLM stack
Benefits
- Fireworks is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Last updated Jul 29, 2026. Interested? Apply on the company site.
About Fireworks AI
Fastest Inference for Generative AI
Use state-of-the-art, open-source LLMs and image models at blazing fast speed, or fine-tune and deploy your own at no additional cost with Fireworks AI!
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