AI Field Engineer, EMEA

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.