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AI & AGENTIC AI ENGINEER PROGRAMME

Engineer the AI Agents Companies Are Racing to Build

A sixteen-week, engineering-first path from LLM fundamentals to production multi-agent systems — RAG, LangChain, LangGraph, MCP and A2A protocols, evaluation, safety and deployment.

16 WEEKS
80 SESSIONS
MON-FRI · 1.5 HRS/DAY
Download Brochure & Syllabus
Prerequisite: working knowledge of Python. No prior machine learning or LLM experience required.
OUTCOMES

What you'll be able to do

LLM APIsRAGLangChainLangGraphMCP / A2AAgentOps
THE PATH

What you'll cover, week by week

01

Python & LLM FoundationsWeeks 1-2

AI systems in Python, LLM APIs, how LLMs behave.

02

Prompt EngineeringWeek 3

Function calling, structured outputs, cost/latency basics.

03

RAG & RetrievalWeeks 4-5

Embeddings, vector databases, hybrid retrieval, reranking.

04

Agent FrameworksWeeks 6-8

LangChain, LangGraph, tool use, agent memory.

05

Multi-Agent SystemsWeeks 9-10

MCP, A2A, orchestrator-worker patterns.

06

Evaluation & SafetyWeeks 11-12

Eval suites, observability, guardrails, prompt-injection defence.

07

Deployment & AgentOpsWeeks 13-14

Docker, sandboxed execution, production monitoring.

08

AI Engineering CommunicationWeek 15

Documentation, demos, stakeholder presentation.

09

Capstone ProjectWeek 16

One agentic system, start to finish — deployed and monitored.

Aravind Reddy Dudipala
Taught personally by Aravind Reddy Dudipala

Senior Data Scientist & AI/ML Programme Leader, 11 years of experience. Instructor at Berkeley Haas and Imperial College Business School. Every batch — up to 50 learners — taught live, session by session.

CAPSTONE

Choose a project in your own domain

Every learner finishes with a deployed, portfolio-ready project — not a certificate for finishing videos.

TRACK 01Autonomous coding agent
TRACK 02Enterprise RAG knowledge assistant
TRACK 03Multi-agent workflow orchestrator
TRACK 04Browser automation agent
TRACK 05Customer support agent with escalation
TRACK 06Research & report assistant agent
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