Build AI Agents
Switch to AI/ML/DS
15 weeks
3 Guided Projects, 10 Capstone Projects to choose from
Generative & Agentic AI practitioners from FAANG and other global Tier 1 companies
Autonomous AI Agents, Multi-Agent Systems, Agentic AI Strategies, RAG, LLM Orchestration, real-world application of Agentic AI, and more
Additional FAANG+ interview preparation for the AI-enhanced roles you’d apply for
Learn from the experiences and mentorship of FAANG+ experts who bring practical, cutting-edge insights to the class.
Cut through the AI noise and master key AI automation skills at your pace, to position yourself at the forefront of Agentic AI.
Learn the full spectrum of Agentic AI, from foundations to real-world deployment, gaining expertise in AI agent workflows and automation.
Focus on tools and AI skillsets most relevant to Software Engineers. Topics include RAG implementation, function calling with various AI APIs, LLM application development and deployment, and AI System Architecture Design.
Gain hands-on training in building AI agents to solve complex industry challenges, such as fraud detection and security threat mitigation.
With an NPS Score of 55 & and average rating of 4.75+ for our AI programs, learners love our structured and hands-on approach.
Build an intelligent healthcare assistant with LangGraph for structured reasoning and medical knowledge integration. This agent handles patient queries, symptom assessment, and information retrieval through conversation. It features contextual memory, appointment scheduling, medication reminders, and personalized advice. Utilize LangGraph for decision trees, RAG for medical knowledge retrieval, and Streamlit for the interface. Tools include Python, LangGraph, Pinecone, and OpenAI APIs.
Build advanced multi-agent AI systems using LangGraph, a framework designed for stateful, context-aware agent orchestration. You’ll explore core concepts like states, nodes, and edges, and understand when to use LangGraph vs. LangChain. Through hands-on coding, you’ll develop a smart travel planner using Supervisor and Swarm architectures. The module also covers API integration to enhance agent capabilities, helping you build scalable, intelligent systems that communicate, delegate, and act autonomously across complex tasks.
Enhance call center operations with a voice-activated chatbot powered by Generative AI. Process audio inputs and generate context-aware responses for seamless customer engagement. Features include multilingual support, sentiment analysis, and conversation memory. Integrate Whisper, OpenAI GPT, and LangChain. Use FFmpeg for audio processing and build UIs with Streamlit and Gradio.
Projects are subject to change as per industry inputs. Choose from one of 10 Capstone Projects.
Build a personalized financial education experience with an AI-powered Finance Assistant that leverages multi-agent LLM systems and Retrieval-Augmented Generation (RAG). The assistant delivers context-aware investment guidance, real-time market insights, and portfolio analysis tailored to each user. Designed for scale, it simplifies complex financial concepts and empowers beginners to make informed decisions.
Accelerate marketing efforts with ContentAlchemy, an AI-powered content creation platform that leverages multi-agent LLM systems to generate high-quality blogs, LinkedIn posts, visuals, and research-driven content. The system uses intelligent agent orchestration to ensure SEO optimization, brand voice consistency, and platform-specific formatting. Designed for creators and businesses, it enables scalable, on-demand content production across multiple formats and channels.
Transform raw call data into actionable insights with an AI-powered Voice-to-Insights system that leverages multi-agent LLMs and speech-to-text technology. The system automatically transcribes, summarizes, and quality-checks support calls, providing structured evaluations and key takeaways at scale. Designed for modern call centers, it standardizes QA processes, improves compliance, and enables faster, data-driven decision-making.
Streamline communication with an AI-powered Email Assistant that uses multi-agent LLM workflows to generate personalized, context-aware email drafts. The system intelligently detects intent, applies custom tone styling, and ensures high-quality output through review and validation agents. Built for productivity and scalability, it enables teams to draft professional emails in seconds while maintaining consistent voice and messaging.
Build an agentic system that automates DevOps workflows through four specialized agents: a Code Analyzer for security reviews, a CI/CD Monitor for deployment oversight, an Infrastructure Scaler for resource management, and an Incident Resolver for system diagnostics. Build it with LangChain, CrewAI, and OpenAI API and integrate with GitHub Actions, AWS Lambda, and containerization tools, while using vector databases and monitoring solutions.
Build an assistant that streamlines healthcare services through four specialized agents: a Symptom Checker for initial assessments, an Appointment Scheduler for EHR/EMR integration, a Medical FAQ Bot for patient queries, and an Insurance Advisor for claims guidance. Use LangChain, GPT-4, and healthcare APIs to create a system that offers comprehensive patient support while maintaining secure data management through VectorDB storage.
Build a comprehensive agentic system utilizing four specialized agents to protect applications: a Vulnerability Scanner for detecting common threats, a Code Security Analyzer for OWASP Top 10 compliance, a Log Analyzer for anomaly detection, and a Compliance Checker for regulatory standards. Use tools like LangChain, OpenAI GPT, and OWASP ZAP to ensure robust security through integrated monitoring and analysis.
Employ four specialized agents to streamline legal document processing: a Contract Analyzer for extracting key elements, a Compliance Checker for regulatory validation, a Case Law Researcher for finding precedents, and a Summary Generator for creating digestible content. Use LangChain, OpenAI, and OCR tools to offer comprehensive legal document analysis through an interactive interface.
Build a multi-agent system designed to automate supply chain processes, including inventory management, demand forecasting, and logistics tracking. The system consists of four agents: a demand forecaster using time-series ML models, an inventory manager analyzing stock levels, a logistics tracker monitoring shipments, and a procurement assistant optimizing supplier contracts. Leverage Python, TensorFlow, XGBoost, LangChain, OpenAI API, SQL/NoSQL databases, and visualization tools like Streamlit in this project.
Enhance software development with an AI-powered pull request (PR) reviewer bot that automates code reviews using Large Language Models (LLMs). This bot provides detailed feedback, identifies bugs, security vulnerabilities, and coding violations, and suggests best practices to streamline the code review process. It improves efficiency and code quality while assisting human reviewers. Integrate with GitHub/GitLab for seamless operation and use models like GPT-4 or Hugging Face Transformers for accurate code analysis. Build with React or Streamlit, and deploy using Docker and AWS for smooth execution.
Streamline the hiring process with an AI-powered assistant that automates resume screening and scoring using large language models (LLMs). This tool evaluates resumes against job descriptions, identifying strengths, weaknesses, and alignment with role requirements. It enhances ATS platforms by providing actionable feedback and recommendations to find the best-fit candidates. Integrate with tools like GPT-4, Gemini Pro, and LangChain for seamless operation. Build a user-friendly interface using React, Node.js, and MongoDB, and deploy it on the cloud with Docker and AWS.
Work on personal or professional projects of your choice. BYOP offers mentorship, structured guidance, and feedback to ensure projects are aligned with industry standards and best practices. It fosters creativity, innovation, and real-world problem-solving, enabling participants to build impactful solutions. You will receive guidance on selecting the right tools and frameworks based on project requirements.
Build a personalized financial education experience with an AI-powered Finance Assistant that leverages multi-agent LLM systems and Retrieval-Augmented Generation (RAG). The assistant delivers context-aware investment guidance, real-time market insights, and portfolio analysis tailored to each user. Designed for scale, it simplifies complex financial concepts and empowers beginners to make informed decisions.
Accelerate marketing efforts with ContentAlchemy, an AI-powered content creation platform that leverages multi-agent LLM systems to generate high-quality blogs, LinkedIn posts, visuals, and research-driven content. The system uses intelligent agent orchestration to ensure SEO optimization, brand voice consistency, and platform-specific formatting. Designed for creators and businesses, it enables scalable, on-demand content production across multiple formats and channels.
Transform raw call data into actionable insights with an AI-powered Voice-to-Insights system that leverages multi-agent LLMs and speech-to-text technology. The system automatically transcribes, summarizes, and quality-checks support calls, providing structured evaluations and key takeaways at scale. Designed for modern call centers, it standardizes QA processes, improves compliance, and enables faster, data-driven decision-making.
Streamline communication with an AI-powered Email Assistant that uses multi-agent LLM workflows to generate personalized, context-aware email drafts. The system intelligently detects intent, applies custom tone styling, and ensures high-quality output through review and validation agents. Built for productivity and scalability, it enables teams to draft professional emails in seconds while maintaining consistent voice and messaging.
Build an agentic system that automates DevOps workflows through four specialized agents: a Code Analyzer for security reviews, a CI/CD Monitor for deployment oversight, an Infrastructure Scaler for resource management, and an Incident Resolver for system diagnostics. Build it with LangChain, CrewAI, and OpenAI API and integrate with GitHub Actions, AWS Lambda, and containerization tools, while using vector databases and monitoring solutions.
Build an assistant that streamlines healthcare services through four specialized agents: a Symptom Checker for initial assessments, an Appointment Scheduler for EHR/EMR integration, a Medical FAQ Bot for patient queries, and an Insurance Advisor for claims guidance. Use LangChain, GPT-4, and healthcare APIs to create a system that offers comprehensive patient support while maintaining secure data management through VectorDB storage.
Build a comprehensive agentic system utilizing four specialized agents to protect applications: a Vulnerability Scanner for detecting common threats, a Code Security Analyzer for OWASP Top 10 compliance, a Log Analyzer for anomaly detection, and a Compliance Checker for regulatory standards. Use tools like LangChain, OpenAI GPT, and OWASP ZAP to ensure robust security through integrated monitoring and analysis.
Employ four specialized agents to streamline legal document processing: a Contract Analyzer for extracting key elements, a Compliance Checker for regulatory validation, a Case Law Researcher for finding precedents, and a Summary Generator for creating digestible content. Use LangChain, OpenAI, and OCR tools to offer comprehensive legal document analysis through an interactive interface.
Build a multi-agent system designed to automate supply chain processes, including inventory management, demand forecasting, and logistics tracking. The system consists of four agents: a demand forecaster using time-series ML models, an inventory manager analyzing stock levels, a logistics tracker monitoring shipments, and a procurement assistant optimizing supplier contracts. Leverage Python, TensorFlow, XGBoost, LangChain, OpenAI API, SQL/NoSQL databases, and visualization tools like Streamlit in this project.
Enhance software development with an AI-powered pull request (PR) reviewer bot that automates code reviews using Large Language Models (LLMs). This bot provides detailed feedback, identifies bugs, security vulnerabilities, and coding violations, and suggests best practices to streamline the code review process. It improves efficiency and code quality while assisting human reviewers. Integrate with GitHub/GitLab for seamless operation and use models like GPT-4 or Hugging Face Transformers for accurate code analysis. Build with React or Streamlit, and deploy using Docker and AWS for smooth execution.
Streamline the hiring process with an AI-powered assistant that automates resume screening and scoring using large language models (LLMs). This tool evaluates resumes against job descriptions, identifying strengths, weaknesses, and alignment with role requirements. It enhances ATS platforms by providing actionable feedback and recommendations to find the best-fit candidates. Integrate with tools like GPT-4, Gemini Pro, and LangChain for seamless operation. Build a user-friendly interface using React, Node.js, and MongoDB, and deploy it on the cloud with Docker and AWS.
Work on personal or professional projects of your choice. BYOP offers mentorship, structured guidance, and feedback to ensure projects are aligned with industry standards and best practices. It fosters creativity, innovation, and real-world problem-solving, enabling participants to build impactful solutions. You will receive guidance on selecting the right tools and frameworks based on project requirements.
FAANG+ Instructors to Train You 
FAQs
What is Agentic AI, and how is it different from traditional AI?
Agentic AI focuses on autonomous systems that operate proactively to achieve goals using LLMs and other tools, without constant human intervention. Unlike traditional AI, which is often reactive and generally requires explicit instructions for each task, Agentic AI understands its environment, thinks through the goals and how to achieve them, makes decisions, takes actions, learns from its experiences, and adapts its behavior over time.
What are the practical applications of Agentic AI?
Applications of Agentic AI include:
Do I need prior AI or ML experience to enroll in this course?
No, prior AI/ML experience isn’t mandatory. However, a strong foundation in software engineering and familiarity with Python/other coding languages are expected. We start with essentials before progressing to advanced Agentic AI concepts.
What kind of projects will I build in the course?
You’ll build hands-on projects like a Financial Bot, Conversational Audio Bot, and choose from 10+ Capstone options (e.g.,Finance Assistant, AI Call Center Assistant, Email Generator). These simulate real-world AI use cases and help build a portfolio for job applications.
How do Capstone Projects help my career?
Capstone Projects are designed with FAANG+ hiring managers in mind. Over 67% of hiring managers now demand to see practical know-how rather than certification or theoretical understanding. They’re reviewed for scalability, robustness, and relevance—showcasing your readiness for AI-enhanced software roles.
Can I bring my own project?
Absolutely. With BYOP, you can work on your unique project idea with mentor guidance, ensuring it aligns with industry best practices and makes your portfolio stand out.
How does this course help me land a FAANG+ job?
Through live sessions led by FAANG+ practitioners, FAANG-focused interview prep, and mock interviews with hiring managers and tech leads. You’ll also build a compelling portfolio with capstone projects reviewed by mentors from companies like Google, Amazon, and Meta.
How much time do I need to commit weekly?
Expect around 8 hours of learning per week. This includes 60+ hours of live sessions, 30+ hours of guided project work, and 21+ hours of specialized sessions over 15 weeks. Bonus content and interview prep sessions are available for those who want to go deeper.
Who are the instructors?
All our instructors are current or former FAANG+ professionals with deep expertise in Generative AI, LLMs, and AI/ML.
What tech stack and tools will I learn?
You’ll work with 30+ industry tools including LangChain, CrewAI, LlamaIndex, Hugging Face, OpenAI APIs, LangGraph, Streamlit, Docker, and Kubernetes—tools widely used in modern AI workflows.
Is the course live or self-paced?
It’s a hybrid format, with weekly live expert-led sessions for core learning and projects, plus self-paced bonus content and career prep modules to support flexible schedules.
How is this different from a typical ML bootcamp?
This course is domain-specific for software engineers—not a generic AI training or prompt engineering course. It focuses specifically on building real-world agentic systems, integrating LLMs with production environments, and preparing for AI software engineering roles, not just research.
What support do I get during the course?
You get access to 1:1 mentoring, career coaching, resume reviews, and mock interviews. Plus, there’s ongoing support from teaching assistants, technical coaches, and peer communities.
What are the career outcomes or placement support offered?
Past learners have landed roles with average packages of over $312K. Our career team offers job targeting strategies, referrals, and personalized application help to support your transition.
What happens if I miss a live session?
All live sessions are recorded and accessible on-demand. You can catch up anytime and even rewatch for revision.
Is there a payment plan?
Yes! We offer multiple financing options to make the course more accessible to working professionals.
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