About This Program
A rigorous career specialization for engineers building dependable generative AI products. The program moves from software and transformer foundations through retrieval-augmented generation, tool-integrated agents, multi-agent workflows, multimodal systems, model adaptation and production LLMOps. Learners repeatedly evaluate quality, cost, latency, safety and security rather than treating model output as inherently reliable. The culminating capstone requires an evidence-based architecture, governed agent behavior, reproducible evaluation, adversarial testing and an operational deployment plan.
What You'll Get
15+ Production LLM System Laboratories
RAG and Agentic AI Engineering
Multimodal and Tool-Integrated Applications
Evaluation, Security and Responsible AI
Faculty-Reviewed Production Capstone
Program Curriculum
1AI Application & Software Engineering Foundations2 Weeks
Key Topics:
Python Typing, Asynchronous Programming and Package Design
FastAPI Services and Application Boundaries
HTTP, JSON, Webhooks and External API Integration
Relational, Document and Cache Data Models
Git, Automated Testing, Dependency Management and Containers
Architecture Documentation, Privacy and Engineering Ethics
2Large Language Models & Transformer Foundations4 Weeks
Key Topics:
Tokenization, Embeddings and Vector Representations
Attention Mechanisms and Transformer Architecture
Pretraining, Instruction Tuning and Model Alignment
Open-Weight and Hosted Model Ecosystems
Decoding, Context Windows, Caching and Generation Controls
Model Selection by Quality, Latency, Cost and Risk
3Prompt Systems & Structured Generation2 Weeks
Key Topics:
System Instructions, Examples and Context Design
Structured Outputs, Schemas and Validation
Function Calling and Reliable Tool Contracts
Reusable Prompt Templates, Versioning and Testing
Task Decomposition, Planning and Verification Patterns
Multilingual Experiences and Failure-Aware Interaction Design
4Embeddings, Vector Search & Knowledge Pipelines3 Weeks
Key Topics:
Embedding Model Selection and Similarity Measures
Document Ingestion, Parsing and Normalization
Chunking Strategies and Metadata Design
Vector Indexes, Filtering and Database Operations
Keyword, Semantic, Hybrid Retrieval and Reranking
Knowledge Freshness, Lineage and Access Control
5Retrieval-Augmented Generation Engineering4 Weeks
Key Topics:
End-to-End RAG Architecture and Data Flow
Query Classification, Rewriting, Expansion and Routing
Context Assembly, Source Attribution and Citation Design
Advanced Retrieval, Graph-Enhanced RAG and Knowledge Graphs
Retrieval Recall, Groundedness and Answer Quality Evaluation
Caching, Latency, Cost and Multi-Tenant RAG Design
6Tool-Using Agents & Workflow Orchestration4 Weeks
Key Topics:
Agent Loops, Tool Selection and Action Observation
Typed Tools, API Connectors and Model Context Protocol
Workflow Graphs, State Machines and Deterministic Control
Short-Term State, Long-Term Memory and Checkpointing
Retries, Idempotency, Timeouts and Failure Recovery
Approval Gates, Sandboxed Execution and Least Privilege
7Multi-Agent Systems, Context & Human Governance3 Weeks
Key Topics:
Router, Supervisor and Delegation Architectures
Agent Communication and Context Boundaries
Shared Memory, Consistency and Conflict Resolution
Planning, Verification and Termination Conditions
Human Oversight, Escalation and Exception Handling
Excessive Agency, Accountability and Operational Limits
8Multimodal, Voice & Code Intelligence3 Weeks
Key Topics:
Vision-Language Models, OCR and Document Intelligence
Speech Recognition, Synthesis and Voice Interaction
Multimodal Retrieval and Cross-Modal Reasoning
Code Generation, Validation and Controlled Execution
Real-Time Streaming and Conversational AI Systems
Accessibility, Modality Selection and Quality Evaluation
9Fine-Tuning, Adaptation & Inference Optimization3 Weeks
Key Topics:
Training Dataset Design, Curation and Quality Assurance
Supervised Fine-Tuning and Parameter-Efficient Adaptation
Preference Optimization and Behavioral Alignment
Distillation, Quantization and Model Compression
Model Serving, Batching, Throughput and Hardware Tradeoffs
Choosing Prompting, RAG, Fine-Tuning or Hybrid Adaptation
10Evaluation, Safety, Security & AI Governance3 Weeks
Key Topics:
Evaluation Datasets, Rubrics, Baselines and Acceptance Criteria
Task Success, Groundedness, Tool-Use and Agent Metrics
Calibrated Model-Based Evaluation and Human Review
Prompt Injection, Tool Abuse and Data Exfiltration Testing
Guardrails, Privacy, Bias, Intellectual Property and Content Risk
NIST AI RMF, OWASP Guidance, Red Teaming and System Cards
11LLMOps, Deployment & Production Reliability2 Weeks
Key Topics:
Model Gateways, Provider Abstraction and Routing
Tracing, Token Accounting, Cost and Latency Observability
CI/CD for Prompts, Models, Tools and Evaluation Suites
Cloud Deployment, Autoscaling, Queues and Semantic Caching
Quality Monitoring, Drift Detection and Incident Response
Fallbacks, Rollbacks, Disaster Recovery and Service Objectives
12Agentic AI Capstone & Career Readiness3 Weeks
Key Topics:
Use-Case Research, Feasibility, Risk and Success Criteria
Knowledge, Model, Memory and Tool Architecture
Production Implementation and Integration Testing
Reproducible Evaluation, Adversarial Testing and Safety Review
Deployment, Monitoring, Incident and Operations Runbooks
Faculty Architecture Review, Demonstration and Capstone Defense
Technical Portfolio, System Design and Engineering Interviews
Course Fee
₹1,05,000/ 9 Months
Limited seats available for next batch.