AI Field Engineer - Enterprise

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 customers whose core product is built on GenAI; size deployments to scale without infrastructure bottlenecks.
• Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles; tune deployments to hit targets.
• Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns.

Model Strategy and Fine-Tuning

• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
• Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets.
• Design and implement evaluation frameworks that measure production-quality metrics.

Customer Engagement and Stakeholder Management

• Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria.
• Own the technical relationship from first engagement through production deployment.
• Spend time on-site with customers, embedding with their teams.

Product Feedback and Platform Improvement

• Identify recurring customer pain points and translate them into concrete product proposals.
• Codify repeatable deployment patterns and contribute them back to internal tooling and documentation.
• Feed customer signals back into the product roadmap.

Minimum 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).
• Demonstrated ability to build production software with customers and ship code running in someone else's production environment.
• Strong Python skills; 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 skills.

Preferred Qualifications

• 10+ years in technical field or engineering roles.
• Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads.
• Experience operating as a technical authority inside a customer's environment.
• Track record taking GenAI POCs from prototype to production-scale deployments.
• Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex).
• Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.