Verified Job
IT / Software / Data Analyst
Agentic AI ILT Trainer / Instructor
IT / Software / Data Analyst
#37880
Remote / WFH
Quantiphi
Job Description
Job Description
Role Summary
We are looking for a practitioner-instructor who has shipped production agentic AI systems and can teach from lived experience. This is not a theoretical course. Participants will be expected to build and defend a production-grade system by Week 10, and the trainer must be equipped to prepare them for that bar.
The trainer is responsible for delivering the knowledge layer across all ten weeks, running engineering clinics at key architectural decision points, and providing mentorship support throughout the cohort.
Key Responsibilities
ILT Delivery (40 hours)
Deliver structured instructor-led sessions covering the full curriculum across Weeks 0–10
Ensure each session gives participants the conceptual and practical grounding they need to complete that week's milestone
Use worked examples, live coding walkthroughs, and domain-grounded scenarios drawn from the insurance claims use case
Build and maintain session materials (slides, notebooks, reference code) aligned to the weekly curriculum
Engineering Clinics (included in the 40 hours)
Facilitate four mandatory engineering clinics at Weeks 3, 6, 7, and 8 (30–45 minutes each)
Review teams' architectural designs before implementation begins (state machines, harness design, multi-agent responsibility maps, judge calibration)
Use a Socratic approach.
Ask questions to surface assumptions rather than prescribe solutions
Identify structural design flaws early and provide actionable correction before teams build on a faulty foundation
Mentorship (25 hours)
Provide async and/or live mentorship support to teams across the 10-week program
Support teams in navigating technical blockers, architectural decisions, and milestone preparation
Participate in (or observe) the three mock client interactions at Weeks 0, 5, and 10 to provide post-session coaching
Required Qualifications
Non-Negotiable
Hands-on experience building and deploying production agentic AI systems (not demos or prototypes)
Proficiency with Google Cloud Platform, specifically Vertex AI, Cloud Run, Firestore, Cloud Trace, Cloud Build, and Secret Manager
Practical experience with agentic frameworks, Google ADK, LangGraph, or equivalent
Experience with RAG system design, including grounding, chunking strategies, retrieval evaluation, and citation-aware response design
Understanding of LLM evaluation methodologies, LLM-as-judge design, judge calibration (Cohen's kappa), and EvalOps pipelines
Familiarity with OWASP LLM Top 10 and AI safety/security practices including prompt injection, PII redaction, and tool poisoning
Prior experience delivering technical training, workshops, or mentorship to engineering audiences
Strongly Preferred
Experience with multi-agent system design, supervisor-worker patterns, A2A handoffs, memory governance
Familiarity with MCP (Model Context Protocol) for enterprise tool integration
Experience with stateful workflow design, state machines, HITL gates, Firestore checkpointing
Exposure to FinOps for LLM workloads, per-call cost tracking, model routing, token optimization
Experience designing streaming UIs for agentic systems (SSE, Next.js or equivalent)
Background in enterprise software delivery, understanding of ADRs, CI/CD pipelines, OpenAPI specs, and production deployment standards
Nice to Have
Domain familiarity with insurance claims processing (FNOL-to-settlement lifecycle, claim types, fraud signals, regulatory HITL requirements)
Experience with GraphRAG, multimodal inputs, or advanced retrieval techniques
What We Are NOT Looking For
Instructors who teach from slides without hands-on delivery experience
Academics or researchers without production deployment experience
Generalist AI trainers without specific agentic systems or GCP depth
Vendors pitching a pre-built curriculum as we have our own curriculum
Engagement Structure
Total duration: 10 weeks (target start: June 7, 2025)
ILT sessions: approximately 4 hours per week, delivered in focused blocks (exact schedule to be agreed with L&D)
Engineering clinics: 4 sessions of 30–45 minutes each at Weeks 3, 6, 7, and 8
Mentorship: approximately 2.5 hours per week, delivered async and/or via live office hours
Mock client interactions: trainer participation/observation at Weeks 0, 5, and 10 (post-session debrief coaching)
Pre-program: 2–3 hours of curriculum alignment and session planning with L&D before kickoff
Compensation: 3 lacs
Role Summary
We are looking for a practitioner-instructor who has shipped production agentic AI systems and can teach from lived experience. This is not a theoretical course. Participants will be expected to build and defend a production-grade system by Week 10, and the trainer must be equipped to prepare them for that bar.
The trainer is responsible for delivering the knowledge layer across all ten weeks, running engineering clinics at key architectural decision points, and providing mentorship support throughout the cohort.
Key Responsibilities
ILT Delivery (40 hours)
Deliver structured instructor-led sessions covering the full curriculum across Weeks 0–10
Ensure each session gives participants the conceptual and practical grounding they need to complete that week's milestone
Use worked examples, live coding walkthroughs, and domain-grounded scenarios drawn from the insurance claims use case
Build and maintain session materials (slides, notebooks, reference code) aligned to the weekly curriculum
Engineering Clinics (included in the 40 hours)
Facilitate four mandatory engineering clinics at Weeks 3, 6, 7, and 8 (30–45 minutes each)
Review teams' architectural designs before implementation begins (state machines, harness design, multi-agent responsibility maps, judge calibration)
Use a Socratic approach.
Ask questions to surface assumptions rather than prescribe solutions
Identify structural design flaws early and provide actionable correction before teams build on a faulty foundation
Mentorship (25 hours)
Provide async and/or live mentorship support to teams across the 10-week program
Support teams in navigating technical blockers, architectural decisions, and milestone preparation
Participate in (or observe) the three mock client interactions at Weeks 0, 5, and 10 to provide post-session coaching
Required Qualifications
Non-Negotiable
Hands-on experience building and deploying production agentic AI systems (not demos or prototypes)
Proficiency with Google Cloud Platform, specifically Vertex AI, Cloud Run, Firestore, Cloud Trace, Cloud Build, and Secret Manager
Practical experience with agentic frameworks, Google ADK, LangGraph, or equivalent
Experience with RAG system design, including grounding, chunking strategies, retrieval evaluation, and citation-aware response design
Understanding of LLM evaluation methodologies, LLM-as-judge design, judge calibration (Cohen's kappa), and EvalOps pipelines
Familiarity with OWASP LLM Top 10 and AI safety/security practices including prompt injection, PII redaction, and tool poisoning
Prior experience delivering technical training, workshops, or mentorship to engineering audiences
Strongly Preferred
Experience with multi-agent system design, supervisor-worker patterns, A2A handoffs, memory governance
Familiarity with MCP (Model Context Protocol) for enterprise tool integration
Experience with stateful workflow design, state machines, HITL gates, Firestore checkpointing
Exposure to FinOps for LLM workloads, per-call cost tracking, model routing, token optimization
Experience designing streaming UIs for agentic systems (SSE, Next.js or equivalent)
Background in enterprise software delivery, understanding of ADRs, CI/CD pipelines, OpenAPI specs, and production deployment standards
Nice to Have
Domain familiarity with insurance claims processing (FNOL-to-settlement lifecycle, claim types, fraud signals, regulatory HITL requirements)
Experience with GraphRAG, multimodal inputs, or advanced retrieval techniques
What We Are NOT Looking For
Instructors who teach from slides without hands-on delivery experience
Academics or researchers without production deployment experience
Generalist AI trainers without specific agentic systems or GCP depth
Vendors pitching a pre-built curriculum as we have our own curriculum
Engagement Structure
Total duration: 10 weeks (target start: June 7, 2025)
ILT sessions: approximately 4 hours per week, delivered in focused blocks (exact schedule to be agreed with L&D)
Engineering clinics: 4 sessions of 30–45 minutes each at Weeks 3, 6, 7, and 8
Mentorship: approximately 2.5 hours per week, delivered async and/or via live office hours
Mock client interactions: trainer participation/observation at Weeks 0, 5, and 10 (post-session debrief coaching)
Pre-program: 2–3 hours of curriculum alignment and session planning with L&D before kickoff
Compensation: 3 lacs
