Audience track
AI Engineer Launchpad
Do not just graduate — become an AI engineer. A complete career-transformation program for 2025 and 2026 graduates, from Python and software engineering foundations through to a production-style capstone and placement assistance.
Positioning
From graduate to AI engineer
This is a complete career-transformation program rather than a basic generative AI course. You start with Python and software engineering foundations — because AI engineering is engineering first — and finish having designed, built, evaluated, secured and deployed a production-style AI system.
The learning journey runs through twelve modules: Python and engineering foundations, generative AI foundations, prompt engineering, embeddings and RAG, AI agents, agent orchestration, A2A and multi-agent systems, evaluation, guardrails and security, production AI engineering, AI developer tools, and the capstone with career preparation.
Who this is for
- 2025 graduates
- 2026 graduates
- B.Tech / B.E.
- MCA / M.Tech
- CS & IT graduates
- ECE / EEE with programming interest
- Freshers targeting AI engineering
Twelve modules
The full learning journey
Each module builds on the previous one, and every stage produces something you can show.
| Module | What you learn | Stack |
|---|---|---|
| 1. Python & software engineering foundation | Python fundamentals, OOP, modules and exception handling; JSON/YAML, REST APIs and HTTP; Git, GitHub, debugging, logging and unit testing; FastAPI, SQL, PostgreSQL, Docker and Linux basics. | Python · VS Code · Git · Postman · FastAPI · PostgreSQL · Docker |
| 2. Generative AI foundations | Generative AI and LLM fundamentals; tokens, context windows, temperature and embeddings; transformers conceptually; LLM APIs, structured output and tool calling; open-source, local and multimodal models. | OpenAI · Claude · Gemini · Ollama · Hugging Face |
| 3. Prompt engineering | Zero-shot and few-shot prompting; role and system prompting; structured JSON output; context management and optimisation; prompt versioning, prompt security and injection defence. | Prompt templates · JSON schema |
| 4. Embeddings, vector search & RAG | Embeddings and semantic search; document ingestion, chunking and metadata; vector databases and similarity search; hybrid search and reranking; RAG architecture, evaluation, security and grounded responses. | LangChain · LlamaIndex · pgvector · Pinecone · Qdrant · Chroma |
| 5. AI agents | Agent architecture, tools, tool calling and memory; planning, reasoning, state management and human-in-the-loop; autonomous agents and production agent patterns. | LangChain · LangGraph · OpenAI Agents SDK · CrewAI · AutoGen |
| 6. Agent orchestration | Sequential, parallel and conditional workflows; supervisor and router agents; planner and executor patterns; human approval, retries, error handling and persistent state. | LangGraph · state stores |
| 7. A2A & multi-agent systems | Multi-agent architecture and agent communication; discovery, delegation and interoperability; A2A concepts and MCP; enterprise agent architecture. | MCP · A2A |
| 8. AI evaluation | Accuracy, relevance, faithfulness, groundedness and hallucination detection; RAG and agent trajectory evaluation; LLM-as-a-judge and human evaluation; golden datasets and automated regression for AI. | Ragas · DeepEval · LangSmith · Phoenix |
| 9. AI guardrails & security | Prompt injection, jailbreaks and data leakage; PII protection; input and output validation; access control, tool authorisation, human approval and OWASP LLM security. | Guardrails AI · NeMo Guardrails · Presidio |
| 10. Production AI engineering | FastAPI, Docker, REST APIs, authentication and API security; Redis, PostgreSQL and vector stores; logging, monitoring, observability, caching and rate limiting; model selection, cost optimisation and CI/CD; AWS deployment fundamentals. | AWS · Docker · Redis · GitHub Actions |
| 11. AI developer tools | AI-assisted coding, debugging, refactoring and test generation; repository understanding, documentation and agentic coding workflows. | Cursor · Codex · Copilot · Claude Code |
| 12. Capstone project | Combine LLM, RAG, agents, tools, evaluation, guardrails, API, UI and deployment — for example an enterprise AI operations agent with a supervisor, knowledge agent, data agent, action agent, evaluation layer, guardrails and human approval. | Full stack |
What you build
A portfolio that escalates
| Stage | Project |
|---|---|
| Foundation | AI chatbot |
| Intermediate | RAG knowledge assistant |
| Advanced | Tool-calling AI agent |
| Advanced+ | Multi-agent system |
| Production | Evaluated, guardrailed, observable AI agent |
| Final | Production-grade enterprise AI application |
Career outcomes
Entry-level AI roles you become eligible for
- Junior AI Engineer
- GenAI Engineer
- AI Application Developer
- LLM Application Developer
- AI Automation Engineer
- RAG Developer
- AI Agent Developer
- Python Developer — AI
- Junior ML/AI Engineer
- AI Solutions Associate
On placement claims. We describe our support as placement assistance, which is what it is: portfolio development, GitHub project review, resume preparation, interview preparation and mock interviews. We do not promise employment.
Questions
For graduates
Before, if you can. The market for entry-level engineering roles now screens for demonstrable AI capability, and arriving with a production-style capstone changes which roles you are eligible for. If you already have an offer, the program still works alongside a notice period or a bench.
B.Tech, B.E., MCA and M.Tech graduates, computer science and IT graduates, and ECE, EEE and related graduates with genuine programming interest. What matters more than the degree is whether you can commit to the Python foundation module properly.
No. We provide placement assistance — portfolio development, GitHub project review, resume preparation, interview preparation, mock interviews and application support. Any provider advertising a guarantee should be asked to put it in a contract.
No. Generative AI courses stop at prompting and a chatbot demo. This runs through retrieval, agents, orchestration, evaluation, guardrails, observability and cloud deployment, and ends in a deployed production-style system. That difference is what interviewers probe.
They care about what you can build and explain. A deployed, evaluated, guardrailed agent platform with a documented architecture is stronger evidence than most internships. The career module teaches you how to present it that way.
Next cohort
Graduate with a portfolio, not just a degree.
Talk to admissions about cohort dates and whether to start before or after your final semester.
Weeknight office hours · lifetime access to recordings · one accountable mentor across all 100 days