Job-Ready Agentic AI Course in
Bangalore (2026): Job-Transition Training for Professionals

Our Agentic AI Course in Bangalore is designed from beginner level to Job-Transition level and reverse-engineered from real Agentic AI Engineer job descriptions hiring across Bangalore, India, UK & USA. You don’t just learn LangGraph; you build a multi-agent HR Screening System, a Customer Support Agent, an Agentic RAG architecture, and a Sales Outreach Agent that uses APIs, , Tools, MCP to perform actions/automations and deployed using CI/CD with Jenkins, Dockers, Kubernetes, AWS, Azure, Testing and Monitored using Langsmiths. 

Course Outcome:

  1. Learning & Developing End-to-End Agentic AI Project with Success Metrics
  2. Learning & Developing Experience on AI Product Manager Roles & Responsibilities
  3. Learning & Developing Experience on Langchains, LangGraphs, Autogen, AWS Bedrock, Crew AI, RAG & Multi- Agents
  4. Learning & Developing Experience on Fine-Tuning, Pretraining LLMs & SLMs
  5. Learning & Developing Experience on Jenkins, Docker, CI/CD, Kubernetes, AWS, Azure, & GCP
agentic ai course in bangalore

Cohort Start Date

28th May, 2026

Live Instructor-Led Classes

Slot:1 - Weekdays: 8PM - 9:30 PM
Slot:2 - Weekends(Sat-Sun): 7AM -10AM

Program Duration

4 Months

Build Agentic AI Github Portfolio with BEPEC

KOL Intelligence & HCP Engagement Engine

Graph-based KOL discovery across publications, trials, and congress talks — with an LLM layer that summarizes physician influence, therapeutic focus, and optimal engagement channel.

Stack: Neo4j · BioBERT · PubMed API · OpenSearch · Claude Sonnet · Veeva CRM · Snowflake

Autonomous Supply Chain Control Tower

Multi-agent system that monitors 12K SKUs across 80 markets, auto-resolves disruptions (port delays, supplier defaults) with planner-executor agents, and only escalates exceptions.

Stack: LangGraph · CrewAI · SAP MCP · Kafka · Neo4j · OR-Tools · Databricks · Power BI

Enterprise MLOps Platform for 180+ Models

Self-service MLOps platform with centralized feature store, model governance, drift monitoring, and auto-retraining — manages 180+ models across 80 markets with 100% SLA.

Stack: MLflow · Feast · Evidently AI · Airflow · Kubernetes · Databricks · Unity Catalog · Grafana

Always-On MMM Platform with GenAI Insights

Bayesian MMM across 40+ markets measuring TV, digital, retail media, and trade spend — with a GenAI co-pilot that explains attribution shifts in plain English to brand managers.

Stack: PyMC · Meridian · Snowflake · dbt · Streamlit · Claude Sonnet · Databricks · LangChain

Adaptive Fraud Detection with Agentic Features

Fraud platform using agentic AI for automated feature engineering — graph neural networks surface mule rings, adaptive thresholds cut false positives by 47% without raising escape rate.

Stack: PyTorch Geometric · Featuretools · Flink · Feast · Redis · Kafka · Snowflake · Neo4j

HR & Recruitment: The "Bi-Directional" Resume Matcher

A system that goes beyond keyword matching to simulate an “Initial Technical Screen” through an agentic conversation with the candidate’s data.

Intelligent Sales Agent for B2B Growth

Agent that prioritizes accounts, drafts personalized outreach from 10-Ks and product signals, books meetings, and preps the AE with a pre-call brief — lifting pipeline by 2.3x.

Stack: LangGraph · Claude Opus · Salesforce MCP · Gong API · Exa · Clay · Pinecone · Temporal

Risk-Based Commercial Auto Pricing Engine

Predictive pricing engine fusing telematics, FMCSA safety, and claims history to produce underwriter-grade quotes in under 200ms with reason codes for every price point.

Stack: XGBoost · GLM · MLflow · Feast · FastAPI · Snowflake · Tableau · Azure Databricks

Cognitive Part-Match Engine for Obsolete SKUs

Multimodal matcher that finds replacements for obsolete industrial parts using specs, CAD drawings, and legacy PDFs — response time drops from days to seconds at ~100% match accuracy.

Stack: CLIP · Claude Sonnet Vision · AWS Textract · Weaviate · LangChain · FastAPI · PostgreSQL

We Help You Make Career Switch into AI

60 - 100% Salary Hike

With BEPEC Portfolio & POCs, Learners can achieve a 60-100% Salary Hike on Average.

500+ Hiring Partners

We refer your profile to 500+ Hiring Partners across India, UAE, UK & USA

30000+ Career Transitions

From 2016 to the present, we made 30K+ Career Transitions

Agentic AI Course - Curriculum

Introduction to AI, Agentic AI & Gen AI
  • Introduction to Artificial Intelligence and its real-world impact
  • Evolution of AI from rule-based systems to deep learning
  • Overview of Machine Learning, Deep Learning, and core algorithms
  • Understanding Generative AI and Foundation Models
  • Working with Large Language Models (LLMs) and Prompt Engineering
  • Exploring Retrieval-Augmented Generation (RAG) for contextual intelligence
  • Introduction to Agentic AI and autonomous intelligent systems
  • Key components of agents — Memory, Tools, Planning, and Decision Nodes
  • Overview of LangChain, LangGraph, AutoGen, CrewAI, and Semantic Kernel
  • Multi-Agent collaboration frameworks and real-world applications in automation
  • Demonstration of Planner–Executor–Evaluator architecture in multi-agent systems
  • Integration of Generative AI with Agentic AI for autonomous decision-making
  • Career pathways and skill roadmap in AI, Gen AI, and Agentic AI
Fastrack Python Mastering from Basics
  • Introduction to Python
  • Why Python, Value, Variable, Function, Library [Roadmap on Python]
  • IDE in Python, Different Data Types
  • List, Tuple, Set & Dictionary Overview
  • Different List Methods
  • Different Tuple Methods
  • Set & Frozenset
  • Dictionary & String Manipulations
  • Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
  • Types of Operators, Conditional Statements
  • While Loop, List Comprehension, Break, Continue, Arguments
  • Functions, Escape Sequences, Lambda Functions
  • Hackathon-1
  • Iterators, Decorators & Generators
  • Modules in Python
  • Creating Custom Python Libraries
  • Lambda, Map, Filter, Reduce
  • Exception Handling
  • File Handling
  • Regular Expressions (Regex)
  • Web Scraping Basics
  • Introduction to OOPS
  • Instance Variable, Class Variable, Class Method
  • Association vs Composition & Aggregation
  • Oops Concept
  • Encapsulation, Inheritance
  • Polymorphism, Method Overloading, Method Overriding
  • Introduction to Pandas
  • Data Analysis using Pandas
  • Introduction to Numpy
  • Different Numpy Commands
  • Introduction to Data Visualisation
  • Data Visualisation using Matplotlib
  • Data Visualisation using Seaborn
  • Data Visualisation using Plotly
  • Why Data Cleaning?
  • Data Cleaning with Sklearn & pandas
  • Regular Expression Basics
  • Mastering Langchains
  • Mastering Tensorflow
  • Mastering PyTorch
  • Mastering Langgraph
  • Mastering Crew AI
  • Mastering Streamlit
  • Mastering Langsmith
Fastrack SQL, Projects with Data Warehouse Concepts
    • What is SQL, RDBMS, and Table Structure
    • Understanding Sprint, Scrum and Agile Project Breakdown in SQL
    • OLTP vs OLAP
    • Data Warehousing Concepts
    • Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)
    • ER Diagrams
    • Data Models like Star Schema and Snowflake Schema
    • DDL vs DML vs DCL vs TCL Commands
    • Basic CRUD Operations — 41:21
    • Different DDL Commands — 41:22
    • Different DML Commands
    • Upsert Operations
    • Different DQL Commands
    • Database Constraints
    • Aggregate Functions, Date Functions and String Functions
    • SQL Joins: Inner, Self, Cross, Left, Right and Outer Join
    • SQL Grouping & Aggregations
    • SubQueries and Types of SubQueries
    • Window Functions in SQL
Applied Statistics (Guided Hands-On)
  • What is Data? Various Types of Data
  • Levels of Measurement
  • Categorical Variables & Visualisation Techniques
  • Continuous Variables and Visualisations
  • Measures of Central Tendency
  • Measuring Skewness
  • Measures of Dispersion
  • Covariance & Correlation Coefficient
  • Box & Whisker Plot
  • Outlier Identification
  • Treating Outliers
  • Sampling Techniques
  • Statistical Analysis Consideration
  • Types of Analytics
  • Sample vs Population
  • Introduction to Inferential Statistics
  • Law of Large Numbers
  • Central Limit Theorem
  • Probability Distributions
  • Conditional Probability
  • Continuous Probability Distribution & Discrete Probability Distribution
  • PMF, CDF, PDF, PPF in Probability
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Exponential Distribution
  • Laplace Distribution
  • Log-Normal Distribution
  • Standard Error
  • Confidence Interval Calculations
  • Z-Table for Confidence Interval
  • T-Table for Confidence Interval
  • Hypothesis Testing
  • Null & Alternate Hypothesis
  • Type-1 Error & Type-2 Error
  • 2-Sample T Test
  • ANOVA Test
  • p-Value Calculations
  • Chi-Square Test
  • Project Demonstration using all the Statistical Techniques
Statistics with Real-Time Project Demonstration on EDA
  • Basics of Statistics
  • Central Limit Theorem, Normal Distribution, Skewness
  • Real-Time Project Demonstration on EDA with Real-World Project
  • End-to-End Statistics using Excel
  •  
Probability Distribution, Hypothesis Testing
  • Probability Distribution, Random Variable, Binomial, Poisson, Exponential
  • Normal Distribution
  • Real-Time Project Demonstration on Probability
  • Hypothesis Testing Part-1
  • Hypothesis Testing Part-2
  • How to Calculate P-Value? Coding Hypothesis Testing
  • How to solve PDF, CDF, PMF?
  • Chebyshev’s, Log, Power Law, Q-Q, CLT
  •  
Feature Engineering Techniques
  • Feature Scaling Techniques
  • Normalisation
  • Standardisation
  • Min Max Scaler
  • Robust Scaler
  • Label Encoding Technique
  • One-Hot Encoding Technique
  • Dummies Technique
  • Ordinal Encoding Technique
  • Imputation Technique
  • Feature Selection Techniques
  • Filter Method: Feature Importance
  • Filter Method: Fishers Score Technique
  • Filter Method: Chi-Square Technique
  • Filter Method: Variance Threshold
  • Filter Method: Correlation Coefficient
  • Filter Method: Mutual Information
  • Wrapper Methods
  • Wrapper Method: Forward Selection Technique
  • Wrapper Method: Backward Selection Technique
  • Wrapper Method: Recursive Feature Elimination Technique
  • Wrapper Method: Exhaustive Feature Selection Technique
  • Wrapper Method: Sequential Feature Selection Technique
  • Embedded Methods
  • Embedded Methods: Regularisation
  • Embedded Method: Elastic Net Regression
  • Embedded Method: Decision Tree
  • Other Techniques
  • Outlier Treatment
  • Over Sampling Technique
  • Under Sampling Techniques
  •  
Applied Machine Learning (Basics To Job-Ready)
  • Types of ML: Supervised, Unsupervised, Reinforcement
  • Pipeline: Data Cleaning → Feature Selection → Modelling → Evaluation
  • Bias-Variance Trade-off
  • Train/Validation/Test Splits
  • Cross-Validation
  • Linear Regression (with gradient descent math)
  • Math behind OLS Technique
  • Assumptions of Linear Regression
  • Evaluation Metrics: RMSE, MAE, R² Score
  • Polynomial Regression
  • Coding Linear Regression Model
  • Regularisation Techniques
  • Why Logistic Regression
  • Math Behind Logistic Regression
  • Evaluation Metrics Behind Classification Algorithms
  • Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, AUC-ROC
  • Coding Logistic Regression Model
  • Introduction to Decision Tree
  • Intuition Behind Decision Tree
  • Math Behind Decision Tree
  • Math Behind Decision Tree using GINI
  • Drawbacks of Decision Tree
  • Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost
  • Coding Decision Tree, Random Forest, GB
  • SMOTE Technique to Handle Imbalanced Dataset
  • Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques
  • Coding Feature Selection Techniques
  • Math Behind KNN
  • Math Behind SVM — 47:20
  • Plotting SVM
  • Introduction to PCA
  • Math Behind PCA
  • Coding PCA
  • AutoML Using Pycaret
  • Clustering: K-Means (Euclidean distance), Hierarchical Clustering
  • Dimensionality Reduction: PCA (Eigen decomposition), t-SNE
  • Anomaly Detection
  • Hyperparameter Tuning: GridSearchCV, RandomSearch
  • Pipelines with Scikit-learn
Unsupervised Learning with Real-Time Projects
  • Supervised vs Unsupervised Learning
  • Fundamentals of Machine Learning Part-1
  • Fundamentals of Machine Learning Part-2
  • Insights from Train & Test Accuracy
  • Associate Rules Part-1
  • Associate Rules Part-2
  • Problem Identification & Approach Designing
  • Associate Rules Scripting
  • Why to do Dimensionality Reduction? Project Implementation
Supervised Learning Algorithms
  • How to Prepare Machine Learning Math for Interviews
  • Why to use Linear Regression?
  • What is Linear Regression
  • Case Study on Linear Regression
  • Math Behind Linear Regression Part-1
  • Math Behind Linear Regression Part-2
  • Math behind OLS Technique
  • Assumptions of Linear Regression
  • Evaluation Metrics for Regression Model
  • Accuracy Improving Techniques
  • Coding Linear Regression Model
  • Regularisation Techniques
  • Why Logistic Regression
  • Math Behind Logistic Regression
  • Evaluation Metrics Behind Classification Algorithms
  • ROC & AUC Curve
  • Coding Logistic Regression Model
  • Introduction to Decision Tree
  • Intuition Behind Decision Tree
  • Math Behind Decision Tree
  • Math Behind Decision Tree using GINI
  • Drawbacks of Decision Tree
  • Random Forest and Gradient Boosting
  • Coding Decision Tree, Random Forest, GB
  • SMOTE Technique to Handle Imbalanced Dataset
  • Filter Method, Wrapper Method, Embedded Method Feature Selection Techniques
  • Coding Feature Selection Techniques
  • Math Behind KNN
  • Math Behind SVM
  • Plotting SVM
  • Additional Topics
  • Introduction to PCA
  • Math behind PCA
  • Coding PCA
  • AutoML Using Pycaret
Natural Language Processing
  • What is NLP? Applications in industry
  • Tokenization, Stopwords, Lemmatization vs Stemming, Normalization
  • Regular Expressions (Regex) in NLP
  • Bag of Words (BoW)
  • TF-IDF Vectorization (Term Frequency–Inverse Document Frequency)
  • Word embeddings: Word2Vec (CBOW, Skip-Gram), GloVe
  • Cosine Similarity
  • N-Gram models (Unigram, Bigram, Trigram)
  • Language Modeling: Probability of a word sequence
  • Smoothing techniques (Laplace, Good-Turing)
  • Parts-of-Speech Tagging (Rules + Statistical)
  • Named Entity Recognition (NER)
  • Chunking and Chinking
  • Using spaCy’s NER pipeline
  •  
MLOps - CI/CD, Docker, K8s
  • End-to-End Deployment Using MLOps & Streamlit
  • Setup Virtual Environment
  • Docker Installation
  • How to Activate Virtual Environment
  • Docker Desktop
  • Regression Model using Pycaret
  • Regression Model using Pycaret Part-2
  • Interpretability of Model using SHAP
  • What is SHAP
  • Application Development using Gradio
  • Creating API using FASTAPI
  • Creating Docker Image
  • Model Versioning using Pycaret & MLFlow
  • End-to-End ML Proof of Concept
Azure ML Studio
  • Introduction to Azure Environment
  • Training & Tuning ML Models using Azure
  • Working with Data in Azure
  • Training ML Models in Azure ML
  • Tuning ML Models in Azure
  • Azure Automated ML
  • Deploying & Explaining ML Models
  • Deploying ML Model for Real-Time Inferencing
  • Deploying ML Models for Batch Scoring
  • Responsible AI
  •  
AWS Sagemaker Studio
  • Setting up AWS Sagemaker & Deploying Simple ML Model
  • AIOps with AWS Sagemaker Studio
Advanced Deep Learning using Tensorflow & PyTorch
  • Building Blocks of Artificial Neural Networks
  • What are Optimisers and Types of Optimisers
  • Math Behind Feed Forward Neural Network
  • Math Behind BackPropagation
  • Why Activation Functions & Importance
  • Introduction to Tensorflow
  • FFNN – Regression Script using Tensorflow
  • FFNN – Classification Script Using Tensorflow
  • Introduction to CNN & Why CNN Algorithm
  • What is Object Detection & Object Classification
  • Intuitive Understanding Behind CN
  • Math Behind CNN
  • CNN Coding using Tensorflow
  • CNN Coding using Keras
  • Types of CNN Networks
  • What is Transfer Learning
  • Tensorflow Hub & Various Pre-Trained Models
  • Using Fre-Trained Models for Object Classification
  • Deploying Image Classification Model in AWS
  • Project Demonstration. Image Classification Project
Computer Vision Mastering Using Tensorflow
  • Introduction to Object Detection vs Object Classification
  • Introduction to CNN
  • Convolution Matrix
  • Max Pooling Layer
  • Point-Wise & Element-Wise Convolution
  • Batch Normalisation
  • Strides & Padding
  • Convolution Filters
  • End-to-End Computer Vision
Natural Language Understanding
  • What is NLU & Why NLU
  • What is POs tagging
  • Case Study: Coding POS Tagging
  • What is Dependency Parsing?
  • What is Constituency Parsing?
  • Case Study: Coding Dependency & Constituency Parsing
  • What is NER? & Usage of NER
  • Case Study: Coding NER
  • What is Coreference Resolution?
  • History of NLU Models
  • Project Demonstration: NLU Classification Model using LSTM
  • What is Encoder & Decoder Model
  • Math Behind Encoder-Decoder Models
Transfer Learning OpenCV & YOLO
  • Introduction to Transfer Learning
  • Headless & With Head Transfer Learning
  • Object Detection using YOLO
  • Object Detection using Open CV
  • Transfer Learning with Tensorflow Hub
Recurrent Neural Networks & LSTMs
  • What is RNN & Why RNN?
  • Limitations of RNN & Why LSTM
  • Intuitive Understanding Behind RNN & LSTM
  • Math Behind RNN
  • Math Behind LSTMS
  • Math Behind GRUS
  • Coding RNN, LSTM, GRU using Tensorflow
  • Project Demonstration: Forecasting Project using LSTMS
  • Project Demonstration: Resume Scoring 
Transformer Models BERT, T5, ELMO
  • introduction to Transformers
  • Scaled Dot Product Attention
  • Multi-Headed Attention
  • Introduction to Transfer Learning
  • Introduction to PyTorch
  • Fine-Tuning Transformers using PyTorch
  • Introduction to BERT
  • Wordpiece Tokenisation
  • Embeddings of BERT
  • Masked Language Modelling Task
  • Project Demonstration: Sentence Prediction Task
  • Types of BERT
  • BERT for Sequence Classification
  • BERT for Question & Answering
Fundamentals of Generative AI
  • What is Generative AI? Applications and Industry Use-Cases
  • Evolution: GANs → VAEs → Transformers → Diffusion → LLMs → MoEs
  • Key Architectures: GPT, BERT, Diffusion, UNet, U-ViT
  • Metrics: FID, BLEU, ROUGE, perplexity, hallucination rate
Generative AI, Prompt Engineering using LangChains
  • What is Generative AI? Use cases in text, image, code, and conversation
  • Foundation Models: GPT, Claude, PaLM, Gemini, Mistral, LLaMA
  • Introduction to LangChains
  • Using LLMs using Lang Chains
  • What is Prompt Engineering
  • Prompt Engineering Principles
  • Zero-shot, Few-shot, Chain-of-Thought (CoT)
  • Best Way to Improve LLMs Accuracy
  • Doing Prompt Engineering using Lang Chains
  • PromptTemplate best practices with LangChain
  • Using ChatPromptTemplate, SystemMessagePrompt,
LangChain Framework
  • Overview of LangChain and Ecosystem
  • Components: Chains, Agents, Tools, Memory, Prompts
  • Integration with OpenAI, Anthropic, Cohere, HuggingFace
  • Use Cases: Doc Chatbot, PDF QA, RAG Chatbots, Tools-based Agents
  • Custom Chains and Agents
Amazon Bedrock & GenAI on Cloud
  • What is Amazon Bedrock?
  • Using Claude, Titan, and Meta Models via Bedrock
  • Prompt Chaining, Guardrails, Evaluators in Bedrock
  • Comparison with Vertex AI (Google), Azure AI Studio
  • Custom Fine-Tuning and Hosting on Bedrock
Retrieval-Augmented Generation (RAG) with LangChain
  • Why RAG? Token limits, context injection
  • Vector Embeddings: OpenAI, HuggingFace, Cohere
  • Vector DBs: FAISS, Pinecone, Chroma, Weaviate, Milvus
  • LangChain’s RetrievalQA Chain
  • Chunking strategies and TextSplitter
  • Hybrid search: Dense + Metadata filtering
  • SequentialChain vs SimpleSequentialChain
  • RunnablePassthrough, RunnableLambda (LCEL)
  • Creating Tool abstractions with @tool decorator
  • Integrating APIs (e.g., SerpAPI, Weather, Calculator)
  • Output parsers: StrOutputParser, StructuredOutputParser
Agentic AI Development
  • What is Agentic AI?
  • Agent Architecture: Planning → Tool Use → Memory → Reflection
  • Tools: AutoGPT, BabyAGI, SuperAgent, CrewAI, AgentLabs
  • Key Capabilities: Decision Trees, Recursive Planning, Dynamic Memory
  • Toolformer & Tool Use via Function Calling
  • Types of agents in LangChain: ReAct, ConversationalReAct, Plan-and-Execute, OpenAIFunctionsAgent
  • Agent memory (ConversationBufferMemory, SummaryMemory)
  • Task Decomposition and Planning
  • Use Cases: Code generation, Auto-documentation, Research Assistants
LangGraph – Multi-Agent DAG Framework
  • What is LangGraph? (Directed Acyclic Graphs for LLM Workflows)
  • Graph Node Design: Single-Agent vs Multi-Agent Nodes
  • Persistent Memory and State Sharing Between Agents
  • Use Case: Build a Modular Multi-Agent PDF Analyst
  • Integration with OpenAI & Anthropic Models
  • What are Multi-Agent Systems (MAS)?
  • Types: Cooperative, Competitive, Mixed-Mode Agents
  • Examples: ChatDev, AgentVerse, CAMEL, OpenDevin
  • Communication Protocols between Agents
  • Real-world Multi-Agent Workflow Simulations
Model Context Protocol (MCP)
  • Definition & Role of MCP in LLM systems
  • Standardising LLM Input/Output via JSON Schemas
  • Tool Usage: Tool Metadata, Function Signatures
  • Comparison: MCP vs LangChain Schemas
  • What is Model Context Protocol (MCP)
  • Context-aware decision making
  • Prompt-as-function vs Function-as-prompt
  • Designing structured workflows (LangGraph overview)
  • Communication between agents
  • Build an MCP-based RAG agent
  • Implement agent-to-agent communication where one retrieves, another summarizes, and a third decides actions
Fine-Tuning and Pretraining Language Models
  • Difference between Pretraining, Fine-Tuning, Instruction Tuning
  • Fine-Tuning Open Source Models (LLaMA, Mistral, Falcon)
  • PEFT Techniques: LoRA (Low-Rank Adaptation) Adapters, Prefix-Tuning
  • Tools: HuggingFace Transformers, PEFT, DeepSpeed, bitsandbytes
  •  
LLMOps & Production Deployment
  • Model Lifecycle: Dev, Eval, Test, Deploy, Monitor
  • LLMOps Stack: LangSmith, PromptLayer, WandB, Evidently, TruLens
  • Logging, Prompt Versioning, Evaluation Metrics
  • CI/CD for Prompts and Chains
  • RLHF, Feedback Loops, Guardrails
  • Combining RAG, Agentic AI, and Fine-tuned models
  • Deploying LangChain applications on: Streamlit, Solara, FastAPI, Gradio
  • CI/CD Pipelines for LLMOps
  • Monitoring and Cost Management (OpenAI + LangSmith + Tracing)
  • Build an End-to-End Custom Knowledge Assistant:
  • Ingest PDFs/Docs → Chunk and Embed → Vector DB Query using Conversational Agent (RAG + Tools + Planner) → Deploy using Solara

AI Agents Course - Why BEPEC?

Academic courses focus on outdated theories. Our Agentic AI Training curriculum focuses 100% on Applied Agentic AI & Generative AI. You will build real AI Agents, deploy RAG (Retrieval-Augmented Generation) pipelines, and automate actual business workflows with various success metrics.

What You Will Master

Our 4-months, hands-on program covers the exact stack hiring managers are prioritizing in 2026:

  • Agentic AI & Multi-Agent Systems: Build autonomous enterprise agents using LangChain, LangGraph, Autogen, Google ADK, Claude SDK, Open AI SDK, AWS Bedrock Agents and CrewAI.

  • LLMOps & Production Deployment: Learn CI/CD for Large Language Models using Jenkins, Docker, Kubernetes, AWS, Azure, Grafana, langsmith

  • Advanced RAG Architectures: Master RAG, Hybrid RAG, Self-RAG, CRAG, Vectorless RAG, Graph RAG, and Different Chunking Strategies. 

  • Tool Calls using APIs & Model Context Protocol (MCP): Build context-aware, real-time troubleshooting agents that integrate seamlessly with enterprise pipelines like Kubernetes, Slack, and Databricks.

Who Should Join This Agentic AI Training in Bangalore?

This Agentic AI training in Bangalore is specifically designed for working professionals planning a career switch or salary hike. Past cohorts include:

  • Software Developers & Java Engineers wanting to move into Agentic AI Engineer roles
  • Data Engineers (PySpark, Databricks, Snowflake) adding GenAI + Agents to their stack
  • Data Scientists & ML Engineers transitioning from classical ML to LLM-powered systems
  • Data Analysts & BI professionals looking for a 60–100% salary jump
  • Testing & QA professionals wanting to escape stagnant roles
  • Product Managers building AI-first products who need technical depth
  • Working professionals from Marketing, HR, Finance, and Operations switching to AI
  • Freshers and final-year students who want to start their career as Agentic AI Engineers

No prior AI experience required. We start with Quick Python fundamentals and take you to deploying multi-agent systems in 4 months.

About Instructor - Rajeev Kanth

With 12+ years of hands-on industry and training experience, Rajeev Kanth is one of India’s leading Agentic AI educators. He has trained over 30,000 professionals worldwide and architected end-to-end AI and Data Solutions for Fortune 500 clients.

  • Delivered 150+ international corporate bootcamps across India, UAE, UK & USA
  • Designed and executed 60+ live AI solutions integrated with enterprise ecosystems
  • Mentored CXOs and Tech Leads on AI adoption strategies and responsible AI governance
  • Featured speaker on “Agentic AI and the Future of Autonomous Systems” at leading AI conferences
  • Hands-on expertise: LangChain, LangGraph, CrewAI, AutoGen, OpenAI, Anthropic, Gemini, Amazon Bedrock, Groq, n8n, LangFlow, FAISS, Chroma, Pinecone, Weaviate, PySpark, Databricks, Snowflake, Kafka, Airflow, AWS SageMaker, Azure ML, Vertex AI, MLflow, Docker, Kubernetes
 

Real-World Projects Developed by Rajeev Kanth

  • Autonomous Enterprise Agents: Designed multi-agent systems using LangGraph for automated supply chain reconciliation and real-time inventory optimization.

  • Scalable RAG Architectures: Implemented high-throughput Retrieval-Augmented Generation for Pharmaceutical Products with advanced chunking and evaluation frameworks (RAGAS, TruLens).

  • Agentic DevOps & SRE: Built self-healing infrastructure agents that monitor, triage, and remediate cloud infrastructure optimization & security incidents autonomously

  • Multi-Modal Generative Workflows: Developing systems capable of generating synthetic customer data, high-fidelity video, and audio for hyper-personalized marketing at scale.

  • MLOps 2.0: CI/CD for LLMs, LLMOps, Model Monitoring, and Versioning with MLflow and Ray.

  • Responsible AI: Implementing bias detection, hallucination mitigation, and compliance guardrails (GDPR, ISO 42001, SOC2).

  • 150+ Corporate Bootcamps: Trusted by like Walmart, Suzlon, DU Dubai, XFab Malaysia, Barclays, Bank of America, Merck, Dell, Yes Bank, Kotak, Nokia, EY, Cognizant, and BMW.

  • Founder of BEPEC: Pioneered the “Experience Building” model, helping thousands of learners transition into AI roles through 60+ live AI solutions and real-time POCs.

Get Agentic AI Certification - Internship & Course Completion

Our Use-case driven Agentic AI course designed to help you stand out in today’s competitive job market. Whether you’re entering the field or upskilling, this program offers a hands-on learning experience that mirrors the demands of leading companies.

From mastering Langchains, Langgraphs, Agentic AI, MCP, LLMOps, RAG, Python, Statistics, Amazon Bedrock, Langchains, OpenAI, Generative AI, Prompt Engineering, Deep Learning and Machine Learning to building predictive models and interpreting real-world datasets, you’ll gain in-demand skills that translate directly to the workplace. Our expert-led training ensures you’re equipped with practical tools, not just theory.

You’ll also complete use cases based on actual industry case studies—giving you the confidence to tackle business problems across domains such as finance, healthcare, and e-commerce.

To ensure you’re ready, the course concludes with a comprehensive assessment inspired by real interview and certification standards.

This certification is valued by over 30+ global employers, including top firms like The MathCo, Walmart, Honeywell, Bank of America, Tiger Analytics, Merck, Allianz, ToTheNew, Yes Bank, Kotak, HDFC, Suzlon —helping you showcase your capabilities and accelerate your career.

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