Trainer: AI Application Developer (GenAI & Agentic AI)

Scholar’sEdge Academy · J. P. Nagar 2nd Phase, Bengaluru · On-site · Full time.

Role at a glance

Teaches: AI Application Developer (5 months + 2-month internship) and the AI Foundations Weekend Bootcamp. Also supports the AI modules of Full Stack Development with GenAI

Reports to: Founder / Academic Head

Schedule: live batches of 3 hours a day, Saturday labs and workshops, and free demo classes. About 45 teaching hours a week, plus prep and mentoring

Experience: Fresher and minimum 1 year in software development, with at least 1 year building GenAI or LLM applications, and at least one AI agent or RAG app deployed live. Teaching or mentoring experience strongly preferred

Pay: ₹15,000 - ₹20,000/- per month full-time.

About Scholar’sEdge Academy

Scholar’sEdge Academy is a skills-and-internship institute in J. P. Nagar, Bengaluru. Every program pairs 3–5 months of live, mentor-led training with a 2-month internship on real projects. Our promise to students is simple: you’ll leave able to show what you can do, not just with a certificate. We’re early, we’re building fast, and the trainer we hire now will shape how we teach AI.

The role

You’ll teach students to build real AI applications, not just call a chatbot API. That means apps that read company documents, use tools, take actions and work through multi-step tasks as AI agents, then get tested, secured and deployed. You’ll teach the “why” behind every tool, so students can still build when the frameworks change next year. You’ll also be the face of our AI courses, in the classroom, in demo classes and in short videos on our social pages.

What you’ll do

Teach

Deliver live, hands-on classes (3 hours a day) covering Python for AI, LLM fundamentals, prompt engineering, RAG, AI agents, evaluation and deployment. Spend most of the time building live, not reading slides.

Run Saturday labs, free weekend workshops, free demo classes and the AI Foundations Weekend Bootcamp.

Teach beginners who know only basic programming, and stretch advanced learners in the same batch.

Build proof with students

- Guide every student through 4–6 portfolio projects and a final capstone, for example a document Q&A; assistant, a customer-support agent with tools, a multi-agent research assistant, or an AI feature inside a web app.
- Make sure every project is deployed live, with a clean GitHub repository, a README, a demo video and basic evaluation results.
- Review code every week. Give written feedback, require fixes and track each student’s progress.
- Mentor students during the 2-month internship: plan real project work, run daily stand-ups and sign off the deliverables.

Keep the course current Own and update the syllabus, assignments,



quizzes and project briefs. AI tools change every few months, so review the course every quarter and replace what’s outdated.

Build marking guides and run mock technical interviews. Help students present their AI projects on resumes, LinkedIn and GitHub.

Be the face of the courses

Appear on camera in 1–2 short teaching videos a week. Scripts and editing support are provided. Examples: “AI agent vs chatbot, explained in 60 seconds” or “Build a bot that answers from your college PDF.”

Answer course questions honestly, and never promise jobs or salaries.

What you must know

- Python and backend

Must have: strong Python (functions, classes, async, virtual environments, packages), REST APIs with FastAPI or Flask, working with JSON, files and databases Good to have: Node.js / TypeScript, background jobs and queues

- LLM fundamentals

Must have: how LLMs work at a practical level (tokens, context windows, temperature, cost and speed trade-offs, why models make things up), LLM APIs (OpenAI, Anthropic, Gemini), prompt design, structured outputs (JSON / Pydantic), function and tool calling, streaming responses Good to have: open-source models through Ollama or Hugging Face, choosing the right model for the job

- RAG (retrieval-augmented generation)

Must have: document loading and chunking, embeddings, vector databases (Chroma, FAISS, pgvector, Pinecone or MongoDB Atlas Vector Search), retrieval and re-ranking, citing sources in answers Good to have: hybrid search (keyword + vector), knowledge graphs (GraphRAG), handling PDFs with tables and images

- Agentic AI

Must have: how agents work (plan, use tools, observe, repeat), building agents with tool use and memory, at least one agent framework (LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen or the OpenAI Agents SDK), multi-step workflows with human approval steps Good to have: multi-agent systems, Model Context Protocol (MCP) servers and clients, browser or computer-use agents

- Quality, safety and cost

Must have: testing and evaluating AI output (test sets, LLM-as-judge, tools like Ragas, DeepEval or LangSmith), guarding against prompt injection, protecting API keys and user data, controlling cost and speed (caching, smaller models, limits) Good to have: tracing and monitoring tools (LangSmith, Langfuse), guardrails libraries

- Shipping

Must have: Git and GitHub,



deploying AI apps (Render, Railway, Hugging Face Spaces, or a cloud platform), quick front-ends with Streamlit or Gradio, environment variables and secrets Good to have: Docker, CI/CD with GitHub Actions, AWS, Azure or GCP AI services, a React front-end for AI apps

- AI-assisted coding

Must have: daily use of GitHub Copilot, Cursor, Claude Code or similar, and the ability to teach students to review and test what these tools produce Beyond the basics (good to have)

Fine-tuning basics (LoRA), multimodal apps (images, voice, documents), basic machine learning concepts

Who we’re looking for

- Freshers and minimum 1 year in software development, with at least 1 year building GenAI apps, and at least one deployed agent or RAG project you can show us live.
- Has taught before: as a trainer, teaching assistant, mentor, college faculty, YouTube creator, or a team lead who onboarded juniors. A strong AI builder with an outstanding portfolio and a great demo class will also be considered.
- Explains hard ideas simply, using everyday examples. Patient with beginners, and honest when they don’t know something.
- Keeps up with AI news, but can tell hype from what actually works in production.
- Comfortable teaching in English. Kannada and/or Hindi is a plus for students and parents.
- Degree in CS, IT, AI/ML or a related field (B.E., B.Tech, BCA, MCA, M.Tech, M.Sc.), or equivalent proven skill.

Not a fit: people who teach from slides only, whose AI experience is only using ChatGPT, who’ve never deployed an AI app, or who think students don’t need to understand what the framework does underneath.

What we offer

- ₹15,000 - ₹20,000/- per month, Paid on time, every month.
- A real say in the curriculum: you’ll help build one of the newest AI courses in Bengaluru.
- An API credits budget of $10 per month for live teaching and demos.
- Your own visible teaching brand, through on-camera content credited to you.

How to apply Send your resume, your GitHub link, one live deployed AI project (an agent or RAG app is ideal) and, if you have one, a short teaching video. Use [email protected] or WhatsApp +91 99020 58603, with the subject “AI Application Developer Trainer”.

Hiring process

- Screening (2 days): we check your resume, GitHub and live AI project.
- Intro call (15 min): experience, availability, salary expectations.
- Demo class (20 min + 10 min Q&A;): teach a topic we send you 48 hours earlier, to real learners or our team.
- Technical round (45 min): a live walkthrough of your AI project’s code and design, plus one small build task.
- Offer (2–3 days): reference check, then the offer letter.

Pay: ₹15,000.00 - ₹20,000.00 per month

Work Location: In person

📌 AI Application Developer (GenAI & Agentic AI) Trainer (Bengaluru)
🏢 Scholar'sEdge Academy
📍 Bengaluru