Highest Paying Advanced 8 Weeks • 55+ Hours • 1-on-1 Live
Generative AI & LLM Engineering (RAG + Agents)
The flagship AI program — build production-grade GenAI systems. RAG pipelines, AI agents, fine-tuning, evaluation and deployment, with the engineering discipline (guardrails, observability, cost control) that separates demos from products.
Industry-Expert Trainer 4.9/5 Student Rating Placement Assistance 24x7 Hands-on Labs
Training Highlights
- 1-on-1 live training with Piyush Mahajan (Microsoft (Azure), Ex-VMware)
- 55+ Hours of live instructor-led sessions — flexible timing, your pace
- Live class recordings with timestamped notes — revise anytime
- 24x7 dedicated lab pod — 8+ guided hands-on labs
- Trainer-written PDF notes, workbooks & cheat sheets
- Mock interviews, resume building & placement assistance
- Course completion certificate with unique ID
- Free demo session before you pay anything
Course Content
APIs, tokens, temperature, structured output, function calling
What You Will Learn — Topic by Topic
- LLM API mastery — OpenAI, Anthropic, open models
- Structured output & JSON mode
- Function calling & tool use patterns
- Embeddings deep-dive & model selection
- Chunking strategies that actually work
- Vector stores — pgvector, Pinecone, Qdrant
- Hybrid search & reranking (BM25 + vectors)
- RAG evaluation — faithfulness & relevance metrics
- Agent architectures — ReAct, planning, reflection
- LangChain/LangGraph workflow building
- Multi-agent patterns & orchestration
- Memory — short-term, long-term & entity memory
- Fine-tuning vs RAG vs prompting — decision framework
- LoRA/QLoRA fine-tuning concepts & demo
- Guardrails — input/output filtering & PII
- LLM observability — traces, evals & monitoring
- Latency, caching & cost optimization
- Deploying LLM apps — Docker, APIs & scaling
Hands-on Labs You Will Perform
LAB 01
Build a function-calling assistant from scratch
LAB 02
RAG pipeline over 500+ document corpus
LAB 03
Agent that browses, computes & reports
LAB 04
Multi-agent research team simulation
LAB 05
Fine-tune a small model on domain data
LAB 06
Eval harness — measure RAG quality objectively
LAB 07
Guardrails + PII redaction pipeline
LAB 08
Capstone — production AI assistant with monitoring
Prerequisites
- Python proficiency
- ML Foundations or equivalent
- API & cloud basics
Who Can Do This Course
- Engineers targeting AI architect roles
- Senior developers & platform engineers
- Tech leads building AI products
Career Opportunities & Salary
India: ₹25 – 50 LPA Global: $150K – $220K
AI Solutions Architect
LLM Engineer
Head of AI / AI Lead
Frequently Asked Questions
Still have doubts about Generative AI & LLM Engineering (RAG + Agents)?
Talk to a counsellor or book a free 1-on-1 demo with Piyush Mahajan — no commitment.