AI Systems & ML Engineering Industry Expert

Requirements

• 8+ years of professional engineering experience , currently at senior/staff/principal level or equivalent (Staff/Principal Engineer, Senior/Staff ML Engineer, Solutions Architect, Forward Deployed Engineer, technical lead).
• You've shipped systems that run in production at real scale , as an employee in an engineering role — not coursework, not side projects, not a slide deck about someone else's platform.
• You can explain why a decision was made, not just how it was implemented — and diagnose and critique someone else's architecture live, on a call, without preparation.
• A public technical footprint: GitHub, conference talks, a book or O'Reilly/Manning title, a technical blog, open-source work, or documented mentorship.
• Strong English (C1+). Sessions and written reviews are in English for a US-based audience.
• Time zone: Americas strongly preferred (US / Canada / LatAm). Defenses are booked in advance, so some flexibility exists — but sessions land in US afternoon and evening hours.
• Comfortable using AI tools in day-to-day technical work.
• Domain depth — one of three tracks
• You don't need all three. Tell us which one is yours.
• AI/ML Engineering. Agentic systems and orchestration (LangChain, LangGraph, CrewAI, ADK), agent reliability and guardrails, MCP; LLM evals — eval harnesses, LLM-as-judge, hallucination metrics; applied fine-tuning (SFT/LoRA/PEFT); LLM observability, A/B experiment design, model serving and inference cost.
• AI Systems Engineering. System and API design, service architecture, cloud and infrastructure (AWS, Kubernetes, Terraform, CI/CD), distributed systems, observability and incident response — plus LLM-powered systems in production: RAG, model serving, fallback paths, cost control.
• Forward Deployed Engineering. End-to-end ownership of deployments in real client or enterprise environments: discovery and scoping under ambiguity, stakeholder management without formal authority, integration with enterprise systems, rollout and adoption — on top of LLM and agent systems in production, RAG over enterprise data, and APIs/integrations.

Nice to have

What we can offer you