Agentic AI ILT Trainer / Instructor

Ask questions to surface assumptions rather than prescribe solutions\nIdentify structural design flaws early and provide actionable correction before teams build on a faulty foundation\n\nMentorship (25 hours)\n\nProvide async and/or live mentorship support to teams across the 10-week program\nSupport teams in navigating technical blockers, architectural decisions, and milestone preparation\nParticipate in (or observe) the three mock client interactions at Weeks 0, 5, and 10 to provide post-session coaching\n\nRequired Qualifications\n\nNon-Negotiable\n\nHands-on experience building and deploying production agentic AI systems (not demos or prototypes)\nProficiency with Google Cloud Platform, specifically Vertex AI, Cloud Run, Firestore, Cloud Trace, Cloud Build, and Secret Manager\nPractical experience with agentic frameworks, Google ADK, LangGraph, or equivalent\nExperience with RAG system design, including grounding, chunking strategies, retrieval evaluation, and citation-aware response design\nUnderstanding of LLM evaluation methodologies, LLM-as-judge design, judge calibration (Cohen's kappa), and EvalOps pipelines\nFamiliarity with OWASP LLM Top 10 and AI safety/security practices including prompt injection, PII redaction, and tool poisoning\nPrior experience delivering technical training, workshops, or mentorship to engineering audiences\n\nStrongly Preferred\n\nExperience with multi-agent system design, supervisor-worker patterns, A2A handoffs, memory governance\nFamiliarity with MCP (Model Context Protocol) for enterprise tool integration\nExperience with stateful workflow design, state machines, HITL gates, Firestore checkpointing\nExposure to FinOps for LLM workloads, per-call cost tracking, model routing, token optimization\nExperience designing streaming UIs for agentic systems (SSE, Next.js or equivalent)\nBackground in enterprise software delivery, understanding of ADRs, CI/CD pipelines, OpenAPI specs, and production deployment standards\n\nNice to Have\n\nDomain familiarity with insurance claims processing (FNOL-to-settlement lifecycle, claim types, fraud signals, regulatory HITL requirements)\nExperience with GraphRAG, multimodal inputs, or advanced retrieval techniques\n\nWhat We Are NOT Looking For\n\nInstructors who teach from slides without hands-on delivery experience\nAcademics or researchers without production deployment experience\nGeneralist AI trainers without specific agentic systems or GCP depth\nVendors pitching a pre-built curriculum as we have our own curriculum\n\nEngagement Structure\n\nTotal duration: 10 weeks (target start: June 7, 2025)\nILT sessions: approximately 4 hours per week, delivered in focused blocks (exact schedule to be agreed with L&D)\nEngineering clinics: 4 sessions of 30–45 minutes each at Weeks 3, 6, 7, and 8\nMentorship: approximately 2.5 hours per week, delivered async and/or via live office hours\nMock client interactions: trainer participation/observation at Weeks 0, 5, and 10 (post-session debrief coaching)\nPre-program: 2–3 hours of curriculum alignment and session planning with L&D before kickoff\nCompensation: 3 lacs\n\nWhat We Provide\n\nDetailed week-by-week curriculum with milestone specifications, supervisor checks, and engineering standards rubric\nFull client data package: synthetic policy documents, claim records, adjuster SOPs, historical resolved claims, and mock enterprise API specs\nGCP project access for session delivery and hands-on demonstrations\nL&D coordination support for scheduling, logistics, and Workday tracking\n\nThis program is designed to produce engineers who can ship agentic AI to production clients. We are looking for a trainer who has done exactly that.