1 week 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: 7 hours agoStatus: Live

Job Summary

Embed with enterprise customers and technology partners to turn complex GenAI problems into production-ready systems. Build end-to-end POCs and MVPs in customer codebases, architect inference foundations, run load tests, and deploy/validate models using inference frameworks like vLLM and SGLang. Lead discovery conversations, manage stakeholders from ML engineers to VPs, and translate field learnings into product improvements and deployment patterns.

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 alongside customer engineering teams inside their codebases, infrastructure, and constraints.
•Architect inference foundations and size deployments to scale without infrastructure bottlenecks, while running load tests and tuning for latency, throughput, and cost baselines.
•Deploy and validate model families on inference frameworks (vLLM, SGLang), selecting optimal shapes, quantization configs, and serving patterns.
•Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation methodology; build and run fine-tuning pipelines and production-quality evaluation frameworks.
•Own the technical relationship from discovery through production deployment, spending time on-site with customers and feeding recurring pain points into product proposals and roadmap updates.

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 that runs in a customer’s production environment.
•Strong Python skills, comfortable debugging production code, with familiarity with Kubernetes and infrastructure engineering.
•Working knowledge of the LLM stack, including inference trade-offs, model serving, and fine-tuning workflows (SFT; DPO/RFT a plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure, plus exceptional communication for discovery and executive discussions.

Experience:GenAIEnterprise AIAI infrastructureCloudLLM

Skills:CommunicationStakeholder managementDebuggingArchitectureDiscovery facilitation

Tech Stack:PythonPyTorchMicrosoft AzureAzure AI FoundryAWSAmazon BedrockSageMakerGoogle Cloud PlatformGCP VertexKubernetesVLLMSGLangTensorRT-LLMSFTDPORFTGPUInferenceFine-tuningLoad testing

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 week 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: 7 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 alongside customer engineering teams inside their codebases, infrastructure, and constraints.
•Architect inference foundations and size deployments to scale without infrastructure bottlenecks, while running load tests and tuning for latency, throughput, and cost baselines.
•Deploy and validate model families on inference frameworks (vLLM, SGLang), selecting optimal shapes, quantization configs, and serving patterns.
•Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation methodology; build and run fine-tuning pipelines and production-quality evaluation frameworks.
•Own the technical relationship from discovery through production deployment, spending time on-site with customers and feeding recurring pain points into product proposals and roadmap updates.

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 that runs in a customer’s production environment.
•Strong Python skills, comfortable debugging production code, with familiarity with Kubernetes and infrastructure engineering.
•Working knowledge of the LLM stack, including inference trade-offs, model serving, and fine-tuning workflows (SFT; DPO/RFT a plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure, plus exceptional communication for discovery and executive discussions.

Experience:GenAIEnterprise AIAI infrastructureCloudLLM

Skills:CommunicationStakeholder managementDebuggingArchitectureDiscovery facilitation

Tech Stack:PythonPyTorchMicrosoft AzureAzure AI FoundryAWSAmazon BedrockSageMakerGoogle Cloud PlatformGCP VertexKubernetesVLLMSGLangTensorRT-LLMSFTDPORFTGPUInferenceFine-tuningLoad testing

Company Brief

Fireworks AI

Industry: SaaS

Website