AI Field Engineer, EMEA

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

Job Summary

Embed with enterprise customers to turn complex GenAI challenges into production-ready systems. Build end-to-end POCs, MVPs, and integrations alongside customer engineering teams, architect inference foundations, and run benchmarking and load tests to validate latency, throughput, and cost. Guide model selection and fine-tuning strategy, lead discovery conversations with stakeholders up to VP level, and translate field learnings into product improvements that accelerate the roadmap.

Location: London

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build end-to-end POCs and MVPs with customer engineering teams inside their codebases, infrastructure, and constraints.
•Architect inference foundations for GenAI-powered products and size deployments to scale without infrastructure bottlenecks.
•Run load tests and establish latency, throughput, and cost baselines, then tune deployments to hit targets.
•Deploy and validate new model families on inference frameworks (vLLM, SGLang), optimizing serving patterns and configurations.
•Lead discovery conversations and manage the technical relationship through production deployment, translating customer pain points into product improvements.

Key Requirements

•5+ years in a hands-on, customer-facing technical role (e.g., Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder).
•Demonstrated ability to build production software with customers, including shipping code running in production environments.
•Strong Python skills, including reading, writing, and debugging production code, with familiarity with Kubernetes and infrastructure engineering.
•Working knowledge of the LLM stack (inference trade-offs, model serving, and fine-tuning workflows such as SFT; DPO/RFT is a plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.

Experience:5+ yearsGenerative AILLM inferenceEnterpriseCloudGPU infrastructure

Skills:CommunicationStakeholder managementRelationship buildingDebuggingDiscovery facilitation

Languages:English

Tech Stack:PythonKubernetesAWSAzureGCPVLLMSGLangTensorRT-LLMSFTDPORFTGPU

Company Brief

Fireworks AI

Develops AI-driven tools to generate and optimize visual marketing content for brands and creators, automating production of short-form videos and multimedia assets for social platforms to improve engagement and scale creative workflows.

Industry: SaaS

Website

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Fireworks AI

1 day ago

AI Field Engineer, EMEA

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

Job Summary

Location: London

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build end-to-end POCs and MVPs with customer engineering teams inside their codebases, infrastructure, and constraints.
•Architect inference foundations for GenAI-powered products and size deployments to scale without infrastructure bottlenecks.
•Run load tests and establish latency, throughput, and cost baselines, then tune deployments to hit targets.
•Deploy and validate new model families on inference frameworks (vLLM, SGLang), optimizing serving patterns and configurations.
•Lead discovery conversations and manage the technical relationship through production deployment, translating customer pain points into product improvements.

Key Requirements

•5+ years in a hands-on, customer-facing technical role (e.g., Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder).
•Demonstrated ability to build production software with customers, including shipping code running in production environments.
•Strong Python skills, including reading, writing, and debugging production code, with familiarity with Kubernetes and infrastructure engineering.
•Working knowledge of the LLM stack (inference trade-offs, model serving, and fine-tuning workflows such as SFT; DPO/RFT is a plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.

Experience:5+ yearsGenerative AILLM inferenceEnterpriseCloudGPU infrastructure

Skills:CommunicationStakeholder managementRelationship buildingDebuggingDiscovery facilitation

Languages:English

Tech Stack:PythonKubernetesAWSAzureGCPVLLMSGLangTensorRT-LLMSFTDPORFTGPU

Company Brief

Fireworks AI

Industry: SaaS

Website