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Artificial Intelligence Engineering Trainer - LLM

Actively Reviewing

VAYUZ Technologies

Coimbatore Full-Time 4–8 yrs exp Posted 1 month ago · Apply by Jul 18, 2026

Job Description

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

(ref:hirist.tech)