The Complete AI Engineering Bootcamp (2026)
16-week AI Engineering Bootcamp: prompts, n8n, Python, FastAPI, LangChain, RAG, LangGraph, MCP, Claude Code and a production AI SaaS capstone.
The Complete AI Engineering Bootcamp (2026) runs 16 weeks, about 60 to 80 hours, with more than 40 projects. It moves from prompt engineering and n8n automation through Python, FastAPI, LangChain, RAG, LangGraph agents, MCP, Claude Code and deployment, and ends with a three-week capstone to build a production AI SaaS application.
Program at a glance
- 16-week structured path in 7 phases
- 40+ hands-on projects
- Production AI SaaS capstone
- Career modules: portfolio, resume, interviews, freelancing
No payment is taken on this site. Outcomes depend on participation and practice; no job, salary or certification outcome is guaranteed.
What you'll learn
- Write structured prompts and evaluate model outputs
- Automate work with n8n and AI agents
- Build APIs with Python and FastAPI
- Build RAG systems with embeddings and vector databases
- Design agents and multi-agent systems with LangGraph
- Expose tools safely through MCP
- Use Claude Code for AI-first development
- Deploy, monitor and evaluate AI applications
Course content
Phase 1: AI foundations (week 1) · 3 topics
- AI fundamentals, LLMs, tokens, context windows, ethics
- ChatGPT, Claude, Gemini, Perplexity, NotebookLM
- Prompt engineering patterns and projects
Phase 2: AI automation (weeks 2-3) · 3 topics
- n8n interface, triggers, expressions, error handling
- AI agent node, memory, tools, webhooks
- AI CRM, support agent, scheduler, sales agent
Phase 3: AI development (weeks 4-5) · 2 topics
- Python, APIs and tooling
- FastAPI, authentication, streaming, Docker basics
Phase 4: AI engineering (weeks 6-10) · 3 topics
- AI system design and LLM APIs
- LangChain, RAG, agents and LangGraph
- MCP and production AI: evaluation, guardrails, observability
Phase 5-6: AI-first development and production (weeks 11-13) · 3 topics
- Claude Code workflow
- Databases, deployment, CI/CD, monitoring
- Multimodal AI
Phase 7: capstone (weeks 14-16) · 2 topics
- Production AI SaaS with FastAPI, RAG, agents, MCP, vector database, Docker, authentication, evaluation and monitoring
- Documentation and final presentation
Bonus modules (self-paced) · 3 topics
- Git, Linux, Docker deep dive, Playwright, OpenTelemetry
- Fine-tuning fundamentals, CrewAI, Google ADK, OpenAI Agents SDK
- AI product strategy, portfolio, resume and LinkedIn, technical interviews, freelancing
Who this program is for
Developers
Add AI engineering skills to your backend or full-stack work.
Data and automation professionals
Move from scripts and dashboards to agents and RAG.
MarTech practitioners
Connect AI agents to customer data and journeys, then see the Agentic AI + MCP track.
Week-by-week plan
| Week | Module | Topics | Projects |
|---|---|---|---|
| 1 | AI, LLMs and prompt engineering | AI vs ML vs DL, LLMs, tokens, context windows, hallucinations, model choice, zero-shot, few-shot, chain of thought, structured outputs, prompt chaining | AI research assistant, resume builder, study assistant |
| 2 | n8n foundations | Nodes, triggers, expressions, loops, error handling, scheduling, Gmail, Google Sheets, Slack, human approval | AI email assistant, resume parser, daily news workflow |
| 3 | Advanced n8n and AI agents | AI agent node, memory, tool usage, webhooks, APIs, multi-step workflows | AI CRM, customer support agent, meeting scheduler, sales agent, recruiting assistant |
| 4 | Python and APIs | Python basics, HTTP, REST, JSON, authentication, SDKs, Claude Code, virtual environments, Postman | Weather API, AI chat script, n8n workflow in Python |
| 5 | FastAPI for AI applications | Routes, validation, authentication, middleware, async, streaming, file uploads, Docker basics | AI chat API, authentication API, file upload API |
| 6 | AI system design and LLM APIs | Request lifecycle, prompt vs workflow vs RAG vs agents, cost, latency, caching, security, multiple model providers | Enterprise AI assistant design, multi-provider chat app |
| 7 | LangChain fundamentals | Chat models, prompt templates, LCEL, chains, output parsers, tool calling, LangSmith | Chatbot, translator, summariser, email generator |
| 8 | RAG with LangChain | Embeddings, chunking, retrievers, hybrid search, reranking, vector databases (Chroma, Pinecone, Qdrant, pgvector) | PDF chatbot, company knowledge base, documentation assistant |
| 9 | Agents and LangGraph | ReAct, memory, tools, state, nodes, edges, multi-agent systems, supervisors, handoffs; overview of CrewAI, OpenAI Agents SDK, Google ADK | Research agent, coding agent, multi-agent workflow |
| 10 | MCP and production AI | MCP architecture, servers, clients, resources, tools, authentication and security; evaluation, guardrails, observability | GitHub MCP, Slack MCP, evaluation dashboard |
| 11 | Claude Code mastery | CLAUDE.md, slash commands, hooks, skills, planning mode, code review, sub-agents, MCP integration | AI coding assistant, refactor an existing project |
| 12 | Databases and deployment | PostgreSQL, Supabase, Redis, storage, authentication, Docker, CI/CD, monitoring, logging | Deploy a production AI application |
| 13 | Multimodal AI | Voice, image and video model families | Voice assistant, AI content generator |
| 14-16 | Capstone: production AI SaaS | Three weeks building one production-grade AI application | AI CRM, support platform, HR platform, knowledge base, voice agent or marketing platform |
Fees and enquiry
CXO Hive batch: starts 2 November 2026, live sessions every Saturday and Sunday, with a 100% live, instructor-led approach.
CXO and executive program fee: ₹1,00,000. Fees are confirmed in writing before payment. No job, salary or certification outcome is guaranteed.
Tool and product names (including Adobe AI features and MCP-based integrations) change quickly. We verify them against current Adobe documentation before each cohort, and training does not guarantee jobs, salary or certification.
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Frequently asked questions
Do I need to know Python first?
No. Week 4 teaches the Python and API basics used in the rest of the bootcamp.
Is this about Adobe?
No. This is general AI engineering. For Adobe-specific agentic work, see the Agentic AI and MCP track.
Do you guarantee a job?
No. The bootcamp builds skills and a portfolio; hiring decisions rest with employers.
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