Artificial Intelligence Engineering Trainer - LLM

Key Requirements :

- 6+ years in AI/ML engineering, LLM product development, or senior technical AI education

- Strong hands-on familiarity with the current LLM tooling ecosystem

- Prior experience designing or governing AI-first learning programs

- Strong understanding of AI-assisted assessments, oral defense models, and trainer calibration

- 5+ years in curriculum design and L&D for technical learners

- Experience conducting train-the-trainer programs and assessment governance

- Exposure to iamneo.ai platform or similar ed-tech delivery platforms is a plus

Required Technical Skills :

- LLM tooling ecosystem : Claude Code, Anthropic APIs, OpenAI, Cursor, GitHub Copilot, v0.dev, Lovable, Bolt.new

- Prompt engineering design patterns : few-shot, chain-of-thought, role-based prompting, negative space, 10-component prompt anatomy

- AI pedagogy frameworks : 70% Rule, UMPIRE framework, AI Tool Usage Charter, blameless AI audit culture

- Curriculum sequencing : prerequisite mapping, spiral curriculum design, backwards design, Blooms taxonomy in AI learning

- Assessment design for AI-augmented environments : AI-permitted exams, oral defense, prompt log evaluation, AI decision journals, live coding with AI + questioning

- Web Dev stack : React, Tailwind, shadcn/ui, react-hook-form, Zod, REST API integration, Zustand

- Python & data stack : Python fundamentals, DSA, NumPy, Pandas, matplotlib, pytest TDD, GitHub Copilot integration

- AI Engineering stack : FastAPI, Docker, GitHub Actions, CodeRabbit, OWASP ZAP

- LMS & ed-tech : iamneo platform administration, SCORM, xAPI, cohort analytics, content

versioning governance

Core Responsibilities :

- Define and govern assessment standards, calibrated rubrics, AI-permitted exam policies, oral defense protocols, and grading calibration

- Ensure strong pedagogical coherence across modules, including the 70% Hands-On / 30% Delivery ratio and consistent use of the 70% Rule

- Lead quarterly curriculum review cycles and ensure content is updated when tools change

- Run train-the-trainer certification programs and define re-certification criteria

- Mentor AI Engineering Trainers through regular content reviews, observed feedback, and grading calibration

- Partner with iamneo leadership to align curriculum milestones with batch delivery timelines

- Design the iamneo AI Pedagogy Framework as the canonical guide for AI teaching, usage, and assessment