🤖 New Era Course

Building Agentic AI Systems
That Actually Work in Production

Go beyond ChatGPT wrappers. Learn to architect, build and interview-ready yourself on multi-agent systems, RAG pipelines, LangGraph, CrewAI and AutoGen — the skills top AI companies hire for right now.

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Live online classes English medium Lifetime recordings Project portfolio

🤖

Who This Is For

Software engineers transitioning into AI engineering roles
Backend/full-stack devs who want to build real agent products
Engineers targeting AI roles at GCC, Singapore & FAANG companies
Anyone interviewing for Agentic AI / LLM engineer positions
Founders building AI-powered products and startups

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No commitment · 30-min assessment call

500+

Engineers Trained

10+

Countries Reached

95%

Placement Rate

Why Now?

The Agentic AI Wave Is Here — Are You Ready?

Companies across GCC, Singapore and globally are desperately hiring engineers who can build autonomous AI systems. This is 2024's version of "knowing cloud" — a must-have, not a nice-to-have.

🧠

Beyond Prompt Engineering

Prompt engineers are a dime a dozen. Engineers who can build tool-calling, memory-aware, multi-step agent pipelines are rare and command 2–3× salary.

🌍

Huge Demand in GCC & Singapore

Noon, Careem, Talabat, ADNOC, and dozens of GCC AI startups are actively hiring Agentic AI engineers. We know exactly what they test in interviews.

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Production-Grade, Not Theory

You'll build real projects: an autonomous research agent, a multi-agent customer service system, a RAG-powered code reviewer — projects you can demo in interviews.

Curriculum

What You'll Master

A battle-tested curriculum built around what AI companies actually interview for

Module 1: LLM Foundations for Engineers Week 1

Transformer architecture internals (what engineers need to know)
Token limits, context windows, embeddings & similarity search
OpenAI / Anthropic / Gemini API — structured outputs, function calling
Prompt engineering patterns used in production systems

Module 2: LangChain & LangGraph Deep Dive Week 2

Chains, agents, tools, callbacks — end-to-end LangChain mastery
LangGraph: stateful multi-step agent graphs with conditional routing
Human-in-the-loop patterns and interrupt handling
Persistence, checkpointing and long-running agent sessions

Module 3: RAG — Retrieval Augmented Generation Week 3

Vector databases: Pinecone, Weaviate, ChromaDB, pgvector
Chunking strategies, embedding models, re-ranking
Hybrid search (BM25 + semantic), HyDE, parent-child retrieval
Evaluation: RAGAS metrics, hallucination detection

Module 4: Tool Use & Function Calling Week 4

Designing tool schemas that LLMs call reliably
Code interpreter, web search, database query tools
Tool error handling and retry logic in agentic loops
MCP (Model Context Protocol) — the new standard

Module 5: Multi-Agent Orchestration Week 5

CrewAI: roles, tasks, process types (sequential / hierarchical)
AutoGen: conversation patterns, group chat, nested chats
Agent routing, handoffs and supervisor patterns
When to use single-agent vs multi-agent architectures

Module 6: Memory & Long-Term Context Week 6

Short-term (in-context) vs long-term (external store) memory
Episodic, semantic and procedural memory patterns
Mem0, Zep, Memgpt — production memory frameworks
Summarisation agents and memory compaction strategies

Module 7: Production Deployment & Observability Week 7

LangSmith / LangFuse tracing — debugging agentic runs
Latency, cost optimisation and caching strategies
FastAPI + async for serving agent endpoints
Safety guardrails, content filtering, rate limiting

Module 8: AI Engineering Interviews Week 8

System design for AI systems: architecture walkthroughs
Live mock interviews replicating Careem, FAANG formats
Common AI engineer interview questions with model answers
Portfolio project review and presentation coaching

Real Projects

Portfolio Projects You'll Build

These go directly on your resume and GitHub — not toy examples

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Autonomous Research Agent

Multi-step agent that searches the web, reads papers, synthesises findings and generates reports

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RAG-Powered Codebase Q&A

Index a large codebase, answer questions, explain functions and suggest refactors

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Multi-Agent Customer Support

Triage agent → specialist agents → escalation agent — full production pipeline

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Data Analysis Agent

Natural language → SQL → chart generation → insight summary pipeline with LangGraph

Reviews

What Engineers Say

★★★★★

"Anurag's mentorship has been truly invaluable. His deep understanding of software engineering and AI, combined with his genuine willingness to help, enabled me to land a great opportunity abroad."

Vaibhav

★★★★★

"Anurag is an excellent mentor with deep expertise in software engineering and the AI domain. His guidance was instrumental in helping me secure a great job abroad."

Pallavi

★★★★★

"He gave me honest feedback about job market in GCC, Singapore, Riyadh. Also we delved into technical details. I would say he is the go-to person for a mentor for GCC."

Suryasis Paul

Building Agentic AI Systems
That Actually Work in Production

Who This Is For

The Agentic AI Wave Is Here — Are You Ready?

Beyond Prompt Engineering

Huge Demand in GCC & Singapore

Production-Grade, Not Theory

What You'll Master

Module 1: LLM Foundations for Engineers Week 1

Module 2: LangChain & LangGraph Deep Dive Week 2

Module 3: RAG — Retrieval Augmented Generation Week 3

Module 4: Tool Use & Function Calling Week 4

Module 5: Multi-Agent Orchestration Week 5

Module 6: Memory & Long-Term Context Week 6

Module 7: Production Deployment & Observability Week 7

Module 8: AI Engineering Interviews Week 8

Portfolio Projects You'll Build

Autonomous Research Agent

RAG-Powered Codebase Q&A

Multi-Agent Customer Support

Data Analysis Agent

What Engineers Say

Ready to Become an AI Engineer?

InterviewClear

Quick Links

Programs

Languages

Contact

Building Agentic AI Systems That Actually Work in Production

Who This Is For

The Agentic AI Wave Is Here — Are You Ready?

Beyond Prompt Engineering

Huge Demand in GCC & Singapore

Production-Grade, Not Theory

What You'll Master

Module 1: LLM Foundations for Engineers Week 1

Module 2: LangChain & LangGraph Deep Dive Week 2

Module 3: RAG — Retrieval Augmented Generation Week 3

Module 4: Tool Use & Function Calling Week 4

Module 5: Multi-Agent Orchestration Week 5

Module 6: Memory & Long-Term Context Week 6

Module 7: Production Deployment & Observability Week 7

Module 8: AI Engineering Interviews Week 8

Portfolio Projects You'll Build

Autonomous Research Agent

RAG-Powered Codebase Q&A

Multi-Agent Customer Support

Data Analysis Agent

What Engineers Say

Ready to Become an AI Engineer?

InterviewClear

Quick Links

Programs

Languages

Contact

Building Agentic AI Systems
That Actually Work in Production