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UPSKILL . EVOLVE . LEAD .
New Batch Starts: December 20, 2025 - Apply Now

AI & Agentic AI
Training Program

Hands-on, project-driven training to build enterprise-grade multi-agent systems.

  • 10 Weeks Hybrid — Core ML → Agentic AI → Capstone
  • Hands-on Labs: LangChain, LangGraph, CrewAI, RAG
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Curriculum — 6 Phases img

A comprehensive journey from foundational ML to advanced agentic AI systems.

  • Mathematics for AI
    Linear Algebra, Probability, Statistics, Optimization, Gradient Descent
    • NumPy
    • SciPy
  • Python for Machine Learning
    Data manipulation, visualization, feature engineering
    • Pandas
    • Matplotlib
    • Scikit-Learn
  • Core ML Algorithms
    Regression, Classification, Clustering, Decision Trees, Random Forest, XGBoost
    • Scikit-Learn
  • Deep Learning Essentials
    Neural networks, CNN, RNN, LSTM, Transformers (intro)
    • TensorFlow
    • PyTorch
  • Model Lifecycle Management
    Training, validation, testing, model evaluation metrics, hyperparameter tuning
    • MLflow
    • DVC
  • Foundation Model Concepts
    What are LLMs, Tokenization, Transformer Architecture, Self-Attention
    • GPT
    • LLaMA
    • Claude
    • Gemini
  • Prompt Engineering
    Instruction prompting, few-shot learning, role-based prompting, chain-of-thought
    • OpenAI Playground
    • PromptLayer
  • Customizing LLMs
    Fine-tuning, LoRA, prompt-tuning, embeddings, adapter layers
    • HuggingFace
    • OpenAI
  • LangChain Basics
    Introduction, Chains, Agents, Tools, Memory, Callbacks, Streaming responses
    • LangChain
  • Vector Databases
    Embedding creation, similarity search, storage, and retrieval
    • FAISS
    • Pinecone
    • Chroma
  • RAG Architecture Overview
    Why RAG is needed, how it improves accuracy and grounding
    • Conceptual frameworks
  • Knowledge Store Design
    Chunking, embedding, retrieval, re-ranking, indexing
    • FAISS
    • Pinecone
    • Chroma
  • Integrating RAG with LLMs
    Context injection, query optimization, hallucination control
    • LangChain
    • OpenAI API
  • Advanced RAG Use Cases
    Enterprise knowledge base search, document Q&A, conversational RAG
    • Custom APIs
  • Enterprise Implementation
    Secured RAG pipelines, compliance, latency optimization
    • AWS Bedrock
    • GCP Vertex AI
  • Introduction to Agentic AI
    Evolution from LLM apps → multi-agent ecosystems
    • Conceptual overview
  • AI Agent Architecture
    Planner, Executor, Memory, Tool Interface, Communication Layer
    • CrewAI
    • AutoGen
    • LangGraph
  • LangGraph Deep Dive
    Graph-based agent orchestration, task flows, message passing, state management
    • LangGraph
  • CrewAI / AutoGen Overview
    Multi-agent collaboration, task delegation, error handling
    • CrewAI
    • AutoGen
  • Model Context Protocol (MCP)
    Tool registration, context passing, execution orchestration
    • MCP Frameworks
  • Orchestrator Implementation
    Multi-agent coordination across LLMs, APIs, voice & data layers
    • Custom SDKs
    • LangGraph + MCP
  • Real-World Integration
    Genesys, AWS Connect, NovaSonic, Twilio, and enterprise connectors
    • Intellectt Universal Connector
  • MLOps Fundamentals
    CI/CD for ML, model versioning, monitoring, A/B testing
    • Docker
    • MLflow
    • Kubeflow
  • Cloud Deployment
    Containerization, Fargate, EKS, Lambda orchestration
    • AWS
    • Azure
    • GCP
  • LLM & Agent Deployment
    Serving LLMs securely, latency control, API gateways
    • FastAPI
    • LangServe
  • Enterprise-Grade DR & Failover
    Designing fault-tolerant AI systems
    • AWS Multi-AZ
  • Responsible AI Practices
    Model interpretability and explainability
    • SHAP
    • LIME
    • Fairlearn
  • Data Privacy & Compliance
    Data privacy, IP ownership, and audit frameworks
    • GDPR Tools
    • CCPA Tools
  • Security & Access Management
    Secure API key and access management for multi-agent setups
    • Vault
    • AWS Secrets Manager

Hands-on Projects & Capstoneimg

Apply your skills through real-world projects and a comprehensive capstone.

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Conversational RAG Bot

Build a context-aware chatbot using RAG architecture with vector databases.

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Research Assistant Agent

Create an autonomous agent that researches, summarizes, and synthesizes information.

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AI Workflow Automator

Design multi-agent systems that automate complex business workflows.

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Speech-to-Speech Orchestrator

Build end-to-end voice AI systems with speech recognition and synthesis.

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Capstone Project

Enterprise Voice Orchestrator

Design and deploy a production-ready multi-agent voice AI system that handles complex customer interactions, integrates with enterprise tools, and scales to handle real-world traffic. This capstone brings together everything you've learned across all 6 phases.

  • Duration
    2 Weeks
  • Team Size
    2-3 Members
  • Deliverable
    Production Demo

Eligibility & Assessment img

Join a cohort of motivated learners ready to master AI and agentic systems.

Eligibility

Candidates with non-traditional backgrounds are encouraged to apply. We value diverse perspectives and experiences.

  • Strong Python programming skills
  • Basic understanding of machine learning concepts
  • Familiarity with data structures and algorithms
  • Bachelor's degree in CS, Engineering, or related field or equivalent experience
  • Passion for AI and willingness to learn

Assessment Process

  • Application Review

    Submit your application with resume and statement of purpose

  • Technical Assessment

    Complete a Python and ML fundamentals assessment (60 mins)

  • Interview

    30-minute technical interview with program instructors

  • Admission Decision

    Receive decision within 5 business days

70Days

Duration

120+

Learning Hours

4 +

Live Projects

30 +

Batch Size

Program Timeline img

10 weeks of intensive, structured learning with hands-on projects

Phases 1-2:

Foundation & Generative AI

4 Weeks

Core ML, Deep Learning, LLMs, Prompt Engineering

Phases 3-4:

RAG & Agentic Systems

4 Weeks

Vector DBs, RAG Architecture, Agent Design, LangChain

Phases 5-6:

Production & Governance

2 Weeks

MLOps, Deployment, Scaling, Ethics, Capstone Project