LivePart 2 · Advanced Track

Gen AI Engineering: Advanced

Part 2 — Advanced Track · 40-Hour Program

An advanced engineering course for learners who already understand LLM APIs, embeddings, vector databases and basic RAG, and now want to build reliable enterprise GenAI systems.

40-Hour ProgramAdvancedAdvanced RAG & LangGraphModel Context Protocol (MCP)Multi-Agent Systems & LLMOpsEnterprise Capstone

*Tentative. Final dates will be announced.

Fee (INR):

₹16,910₹33,820

50% OFF · Limited Time

Overview

Move Beyond Prototypes: Build Production-Ready Enterprise GenAI Systems.

Move beyond prototypes: build GenAI systems that are grounded, secure, observable, scalable, and production-ready. This 40-hour instructor-led advanced course dives deep into hybrid search and cross-encoder reranking, fine-tuning with LoRA/QLoRA, stateful LangGraph workflows, multi-agent coordination, Model Context Protocol (MCP) servers, multimodal document AI, prompt injection defense, evaluation with golden datasets, and scalable LLMOps architectures.

Core Technology Scope

Advanced RAG, Hybrid Search & RerankingFine-Tuning, PEFT & LoRA AdaptationAI Agents & Enterprise Tool CallingLangChain, LangGraph, Memory & StateMulti-Agent Systems & Reliability ControlsModel Context Protocol (MCP) Client & ServerMultimodal AI & Document Vision RAGAI Security, RBAC & GovernanceEvaluation, Golden Datasets & TracingLLMOps, Scaling & Enterprise Capstone

Key Advantages

Move Beyond Prototypes to Production-Grade GenAI Systems

✓

Production-Grade Advanced RAG

Master hybrid search (BM25 + dense vectors), query transformations (HyDE), cross-encoder reranking, and context compression to solve real-world retrieval failures.

✓

Fine-Tuning & Adaptation Decisions

Learn a rigorous decision framework to choose between prompting, RAG, and fine-tuning. Train low-rank adapters with LoRA and QLoRA.

✓

Stateful LangGraph Workflows

Build robust, deterministic stateful agent workflows with checkpoints, pause/resume, persistence, and human-in-the-loop approvals.

✓

Model Context Protocol (MCP) Standards

Implement both MCP clients and MCP servers in Java/Spring AI, standardizing enterprise tool and data connectivity with least-privilege security.

✓

Multi-Agent Coordination & Swarms

Design supervisor-worker and hierarchical multi-agent architectures with strict context boundaries, token budgets, and loop prevention.

✓

Enterprise Security, Evals & LLMOps

Defend against prompt injection, secure RAG with tenant RBAC, benchmark against golden datasets using LLM-as-a-Judge, and deploy scalable architectures.

Target Audience

Who Is This Program For?

Graduates of Part 1 (Foundation) ready to master autonomous agents and enterprise architectures.
Senior Software Engineers & Architects designing production GenAI platforms and microservices.
Backend Developers looking to build stateful LangGraph workflows and Model Context Protocol (MCP) servers.
Tech Leads and Engineering Managers evaluating fine-tuning vs RAG, LLMOps pipelines, and AI security governance.
Enterprise Developers needing robust evaluation datasets, tracing, and multi-tenant access control for AI systems.

Prerequisites

Starting Requirements

Completion of Part 1 (Foundation) or prior practical experience with LLM APIs, embeddings, vector databases, and basic RAG. Familiarity with Java or Python is recommended.

Level: Advanced — Part 1 or equivalent required

Curriculum Breakdown

What You'll Learn

Structured modules designed to build competencies systematically.

•Hour 1 — Hybrid Retrieval: Semantic vs keyword retrieval, BM25 and vector search, hybrid score fusion, metadata-aware retrieval [Hands-on: Implement a hybrid retrieval flow]

•Hour 2 — Query Transformation: Query rewriting and expansion, multi-query retrieval, query decomposition, conversational rewriting and HyDE concepts [Hands-on: Compare raw queries with transformed queries]

•Hour 3 — Reranking: Two-stage retrieval architecture, cross-encoder reranking, LLM-based reranking, Top-K vs final-K selection [Hands-on: Add reranking and measure relevance changes]

•Hour 4 — Context Optimization & Advanced Patterns: Context compression and deduplication, context ordering and token budgets, parent-child and multi-vector retrieval, corrective, agentic and graph-based RAG concepts [Hands-on: Design an advanced RAG architecture]

•Hour 5 — Advanced RAG Project: Query rewrite, hybrid search, reranking, context compression, citations and evaluation [Hands-on: Build and compare Basic RAG vs Advanced RAG]

Outcome: Build production-grade retrieval architectures combining hybrid search, query transformations, cross-encoder reranking, and context compression.

Applied Methodology

Learn by Building

Generative AI is best understood by building with it. The program will emphasize practical implementation and real-world application rather than purely theoretical learning.

Advanced RAG

Fine-Tuning

LangGraph Agents

Multi-Agent Swarms

MCP Integration

Multimodal AI

Security & Governance

LLMOps & Enterprise Capstone

Real-World Applications

Projects & Practical Deliverables

Build and work with practical applications directly derived from our curricula.

BM25 · Dense Vectors · Cross-Encoder Reranker · HyDE · Spring AI

Project 1: Advanced RAG Pipeline

Hybrid retrieval, query rewriting, reranking, context compression and citations.

Focus: Hybrid retrieval, query transformation, cross-encoder reranking, context compression

LoRA / QLoRA · PEFT · Hugging Face · Dataset Curation · Evaluation

Project 2: Fine-Tuning Experiment

Compare prompt-only, RAG and parameter-efficient adaptation approaches.

Focus: Parameter-efficient adaptation, instruction datasets, loss & latency benchmarking

Tool Schemas · ReAct Loop · REST API Tools · Spring AI

Project 3: AI Support Agent

Use multiple tools for knowledge search, account lookup and workflow actions.

Focus: Schema-driven tool calling, error recovery, enterprise service execution

LangGraph · State Machines · Checkpoints · Human-in-the-Loop

Project 4: LangGraph Stateful Agent

Implement state, persistence, retries and human approval.

Focus: Stateful orchestration, checkpoint persistence, pause/resume, validation

Supervisor Pattern · Handoff Protocols · Token Budgets · Guardrails

Project 5: Multi-Agent Workflow

Coordinate supervisor and specialist agents with cost and loop controls.

Focus: Multi-agent delegation, context boundaries, termination rules, cost control

Model Context Protocol · MCP Server / Client · Spring Boot · RBAC

Project 6: MCP Enterprise Integration

Expose enterprise services through MCP and consume them from an AI application.

Focus: Standardized tool connectivity, discovery, authorization, security auditability

Vision-Language Models · Layout Extraction · Table Parsing · Multimodal Indexing

Project 7: Multimodal RAG

Combine documents, images and structured extraction.

Focus: Scanned PDF parsing, layout-aware extraction, visual Q&A

Spring AI · LangGraph · MCP · Advanced RAG · Security RBAC · OpenTelemetry

Project 8: Enterprise Capstone

Build a secure, observable and production-ready AI Knowledge & Operations Assistant.

Focus: Full-stack enterprise AI architecture, golden dataset evaluation, production scaling

End-to-End Capstone

Enterprise Capstone: AI Knowledge & Operations Assistant

Build a secure, observable and production-ready AI Knowledge & Operations Assistant integrating Advanced RAG, tool-calling agents, MCP server integrations, and enterprise security controls.

Integrated Architecture Workflow

1.Architecture & Plan: Formulate enterprise knowledge and operations assistant specifications across documents, APIs, and databases

2.Advanced Retrieval: Implement hybrid BM25/vector search, query transformations, cross-encoder reranking, and compression

3.Stateful Agent & MCP: Deploy LangGraph stateful agent workflows and build custom MCP servers with human approvals

4.Security & Governance: Implement tenant-level RBAC, prompt injection defenses, PII masking, and least privilege tool allowlists

5.Evaluation & Observability: Run golden-dataset benchmarking, LLM-as-a-Judge evaluations, and distributed request tracing

6.Production Review & Final Demo: Present final platform architecture, defend design trade-offs, and outline scaling from 100 to 1M users

Program Specifications

Instructor-Led Live Cohort + Hands-On Labs

Detail	Information
Track	Part 2 — Advanced Track
Format	Instructor-led + Hands-on Labs
Duration	40 Hours (11 Modules, 40 Hourly Sessions)
Level	Advanced — Part 1 or equivalent required
Core Stack	Advanced RAG, Fine-Tuning, Agents, LangGraph, Multi-Agent, MCP, Security, LLMOps
Prerequisites	Part 1: Foundation or equivalent GenAI & RAG experience
Capstone	Enterprise AI Knowledge & Operations Assistant
Launch	Tentatively 2026

Interactive Cohort Model

Learn Enterprise AI Engineering With Live Cohort

✓

Architectural Live Sessions

Interactive lectures and code walkthroughs covering all 40 hours of advanced content.

✓

Hands-on Labs in Every Hour

Build hybrid search pipelines, LangGraph graphs, MCP servers, and evaluation suites directly in code.

✓

Production Hardening & Code Reviews

Tackle prompt injection, latency bottlenecks, token budgets, and distributed tracing with senior guidance.

✓

Uncorp Community

Collaborate with senior software engineers, architects, and instructors in our private WhatsApp channel.

Package Breakdown

Everything You Need for the Journey

40 Hours of advanced instructor-led live sessions

Hands-on labs in every single hour

8 practical projects including Enterprise Capstone

Production-grade code repositories, LangGraph templates & MCP server codebases

Uncorp WhatsApp Community & mentor access

Instructor Experience

Learn From Industry Experience

Learn from an experienced technology professional with 12+ years of industry experience.

Community Support

Learn With the Uncorp Community

Join the Uncorp WhatsApp Community and connect directly with fellow AI builders, alumni, and instructors.

*Uncorp does not promise 24/7 support, guaranteed response time, personal mentoring, or 1-on-1 private doubt solving unless formally announced.

Uncorp WhatsApp CommunityLearner & Team Network

Status Overview

Program Information

Detail	Status
Program	Gen AI Engineering: Advanced
Track	Part 2 of 2 (Advanced Track)
Format	Instructor-led + Hands-on Labs
Duration	40 Hours
Curriculum	11 Modules, 40 Hourly Sessions
Projects	8 Hands-on Deliverables + Capstone
Schedule	Tentatively 2026
Price	₹16,910₹33,82050% OFF

Enrollment

Program Pricing & Enrollment

Inaugural advanced cohort. Early registrations receive access to prep material and community onboarding.

50% LIMITED TIME OFFER

Program Fee (INR)

₹16,910₹33,820

You Save ₹16,910 (50% OFF)

Special cohort pricing · 100% money-back satisfaction guarantee

Includes:

Priority enrollment in the inaugural advanced cohort
All live session recordings with cohort access
8 advanced project codebases, MCP servers & evaluation datasets
Uncorp WhatsApp Community membership

Got Questions?

Frequently Asked Questions

Explore More Paths

Related Programs

Gen AI Engineering: Foundation

Part 1 (40 Hours): AI foundations, LLM mechanics, Spring AI, vector DBs, and Enterprise RAG.

View Program →

Java Backend Development with Gen AI

Build enterprise Spring Boot systems integrated with Spring AI, RAG, and MCP.