Note: The job is a remote job and is open to candidates in USA. TripleTen is an EdTech company that designs and runs technology career learning programs for the US and Latin American markets. The AI Systems & ML Engineering Industry Expert will set the technical standard for advanced engineering programs by reviewing project defenses, chairing mock review boards, hosting live design sessions, and advising instructors on complex technical decisions.
Responsibilities
- Sit on final project defenses. Review a deployed system, a distributed-systems capstone, an agentic architecture, or a client-facing delivery package against the rubric — then run the defense and give structured, senior-level critique
- Chair mock review boards and executive-panel presentations. Architecture review boards, model and system reviews, exec go/no-go presentations, depending on the program
- Host one or two live sessions a month on the design and decision layer of your domain: where systems split, how they fail, which tradeoff to make and why
- Set the technical standard for the instructors running weekly delivery, and act as their escalation point on the hard design calls
Skills
- • 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
- 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
- • You've already run technical sessions in some form: internal tech talks, conference workshops, engineer onboarding, or mentoring
- • Hands-on ownership of an eval or observability stack in production, not just usage of one
- • Experience being the primary technical resource embedded with a customer team (for the FDE track)
Benefits
- Your name and profile featured as an Industry Expert on the program page.
- A peer-level audience. A defense is a technical review with a working engineer, not homework grading.
- A genuinely small commitment: 4–10 hours a month, with slots booked about two weeks ahead and the option to pause at any time.
- Fully remote, with a small international team and no micromanaging.
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