AI Field Engineer
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.
About the job
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.
Skills
Python, Kubernetes, AWS, Azure, GCP, Gpu Infrastructure, vLLM, Sglang, Tensorrt-Llm, Sft, Dpo, Rft, Llm Inference, Fine-Tuning, Agentic Systems
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