Role Summary
We are looking for an experienced and impact-driven Master Trainer in Artificial Intelligence to lead the Google DeepMind Faculty Train-the-Trainer (TtT) initiative under AVPN Phase 3.
This is a training-of-trainers role. Rather than teaching learners directly, the Master Trainer will build the capability of Anudip’s faculty and partner-college faculty so they can confidently deliver the Google DeepMind AI curriculum to their own learners with technical accuracy, strong facilitation, and responsible-AI practices. The role combines deep AI subject-matter expertise with instructional design, faculty mentoring, and academic quality assurance — ensuring consistent, high-quality delivery across cohorts and locations.
Program Context
The role supports the rollout of a Google DeepMind–aligned AI curriculum through a college model: the Master Trainer trains and certifies faculty at Anudip and partner colleges, who in turn deliver the curriculum to their own students. This emphasises scalable, standardised faculty enablement across multiple college locations. Based on the course focus, faculty will need to be trained to deliver:
- AI & Machine Learning foundations – how learning systems work, key ML concepts, and neural network basics.
- Generative AI & applied tools – large language models, prompting, and hands-on use of tools such as Google Gemini and Colab.
- Responsible AI – fairness, safety, transparency, and ethical use of AI (a core emphasis of DeepMind’s approach).
- Applied / project-based learning – practical, employability-focused AI use cases for first-generation and diverse learners.
Note: Curriculum scope above is indicative and to be finalised against the shared Google DeepMind course content once the syllabus is confirmed.
Primary Objectives
- Build a pool of certified, delivery-ready AI faculty through structured Train-the-Trainer (TtT) sessions.
- Ensure faculty can deliver the Google DeepMind curriculum with technical accuracy and consistent quality across cohorts.
- Embed Responsible AI principles and practical, hands-on pedagogy into every faculty delivery.
- Enable measurable improvement in trainer competency, session quality, and downstream learner employability outcomes.
Key Responsibilities1. Train-the-Trainer Delivery & Faculty Enablement
- Design and deliver structured TtT sessions that build both AI subject knowledge and facilitation skills.
- Certify and onboard faculty against a defined competency framework before they deliver to learners.
- Conduct live demonstrations, model teaching sessions, and guided practice-teach-back exercises.
- Provide ongoing coaching, feedback, and refresher sessions to maintain delivery standards.
- Train and certify faculty at partner colleges to deliver the Google DeepMind AI curriculum within their own institutions.
2. Curriculum Contextualisation & Content Development
- Translate the Google DeepMind course content into structured, delivery-ready lesson plans, facilitator guides, labs, and assessments.
- Develop trainer toolkits, model answers, rubrics, and capstone project briefs.
- Simplify complex AI and ML concepts into learner-friendly formats suitable for diverse, first-generation learners.
3. Academic Quality & Program Alignment
- Monitor session quality through classroom observation, sample audits, and learner feedback.
- Align curriculum, projects, and assessments with entry-level AI-enabled job roles and current hiring trends.
- Support faculty screening and technical interviews for the AI program.
- Report progress, competency data, and quality metrics in line with program and CSR/donor requirements.
Technical Competency RequirementsMust-Have
- AI & Machine Learning Foundations
◦ Solid grasp of core ML concepts: supervised/unsupervised learning, model training, evaluation, and neural network basics.
◦ Ability to explain how modern AI systems (including deep learning and transformers) work at a conceptual level.
- Generative AI & Applied Tools
◦ Hands-on experience with LLMs and generative AI tools (e.g., Google Gemini) and structured prompt engineering.
◦ Practical use of Python and Google Colab for AI demonstrations and applied exercises.
◦ Understanding of AI safety, fairness, bias, transparency, and data-ethics, with the ability to teach these practically.
- Training & Instructional Skills
◦ Proven Train-the-Trainer / faculty-development capability and strong facilitation skills.
◦ Experience in curriculum design, lesson planning, and assessment development.
Good-to-Have
◦ Exposure to deep learning frameworks (TensorFlow / PyTorch) or DeepMind-related learning resources.
◦ Familiarity with reinforcement learning or notable AI applications (e.g., AlphaFold-type case studies) for teaching context.
◦ Relevant certifications in AI/ML, Generative AI, or Google AI/Cloud credentials.
◦ Experience delivering programs in CSR, NGO, or skilling environments for diverse learners.
Illustrative Projects the Trainer Should Be Able to Deliver & Mentor
- A prompt-engineered AI assistant / chatbot built using Gemini for a real-world use case.
- A simple ML classification or prediction model demonstrated end-to-end in Colab.
- A Responsible-AI case study: identifying and mitigating bias in a given scenario.
- A capstone project brief and rubric that faculty can run with their own learner cohorts.
Qualifications & Experience
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, IT, or a related discipline.
- Minimum 2+ years of practical experience in AI/ML delivery, technical training, or curriculum development, with strong hands-on Generative AI skills.
- Prior Train-the-Trainer, faculty-mentoring, or facilitation experience preferred.
- Relevant AI/ML or Generative AI certifications are an added advantage.
- Experience in CSR, NGO, or government skilling initiatives is an added advantage.
Pay: ₹35,000.00 - ₹55,000.00 per month
Benefits:
- Health insurance
- Provident Fund
Work Location: In person
