Build Production-Grade Agentic AI Systems & Crack Software Engineering Roles

Build and run production-grade agentic AI systems used by top engineering teams across real-world use cases.

Built for software engineers who want to own agentic systems end to end, not just experiment with GenAI tools.

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Program Overview

Who This Is Built For

SWEs across Backend, Frontend, Full-Stack, Platform, and Test roles
Senior ICs moving into agentic AI and AI-first system design
Engineers aiming to own AI systems end to end

Program Duration

42-week integrated program: Agentic AI foundations through MAANG+ interview readiness
Progresses from agent fundamentals to enterprise multi-agent systems at production scale
Designed to run alongside a full-time engineering role

Live Learning

80+ hours of live instruction led by MAANG+ engineers
30+ hours of expert-guided live hands-on projects
21+ hours of power-packed specialised sessions

Projects

9 live, expert-guided, end-to-end agent build projects
12 production-grade capstone architectures to choose from
Real systems — RAG pipelines, multi-agent workflows, not demos

Instructors

700+ MAANG+ engineers and architects building agentic systems in production
Learn from practitioners with an active production system ownership
Grounded in real hiring patterns and engineering decisions

What You’ll Build and Learn

Production-ready RAG pipelines, reflex agents, and multi-agent workflows
Multi-agent orchestration and structured protocols (MCP, A2A, ACP)
Evaluation, observability, safety guardrails, and cost control

Systems-First Capstone Architecture

Design a complete enterprise multi-agent system end-to-end
Cover orchestration, memory, evaluation, safety, and CI/CD
Built the way real AI platforms are shipped

Agentic AI & Domain-specific Interview Prep

Structured prep for MAANG+ Full Stack Engineering roles
Practice agentic system design and architecture discussions
15 (Domain Interview Prep) + 5 (Agentic Interview Prep) mock interviews

Average Salary

0 LPA

Professionals trained

0 +

Average ROI on course price

5x- 5 x

30+ Tools & Tech You’ll Learn

Why SWEs Choose This Applied Agentic AI Program

Built for Real Production Systems

Design and build agentic AI systems through hands-on, guided projects
Mirror how multi-agent systems run in real engineering teams
Move beyond demos to production-ready architectures

What SWEs Actually Build at Work

Learn agentic design patterns used in modern AI-first codebases
Work with RAG pipelines, multi-agent orchestration, and structured outputs
Apply system-level thinking directly to backend and platform workflows

Designed Specifically for Software Engineers

Focus on function calling, deployment workflows, and system architecture
Build reliability, observability, and performance into agentic systems
Prepare for AI-first SWE roles, not research or tooling-only tracks

Learn from Engineers Building It Today

Guided by 700+ MAANG+ engineers and architects
Learn directly from practitioners operating agentic systems in production
Gain insights grounded in real ownership and on-call realities

Hands-On, Production-Style Projects

Build systems aligned with real industry workflows
Emphasize reliability, evaluation, safety, and cost control
Ship architectures that resemble real AI platforms

Results Engineers Trust

Trusted by thousands of engineers globally
NPS of 55 with an average learner rating of 4.75+
Outcomes driven by real system builds, not theory

This is a living curriculum, continuously updated to reflect how agentic AI systems are built and operated in 2026 and beyond.

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Detailed Curriculum: Applied Agentic AI for SWEs

Applied Agentic AI Core for SWEs

Week 0: Pre-Program Foundations

Colab setup, OpenAI APIs, and secure key management
Data handling with Pandas and strict system prompt design
End-to-end agent flow: input, reasoning, data access, response

Outcome: Build a Data-Grounded CSV FAQ Agent.

Week 1: Agentic AI Foundations & Reflex Agents

Evolution from rule-based systems to LLM-based agents
Agent anatomy: brain, planning, memory, tools, and ReAct loop
Prompt engineering, agentic design patterns, and tool use

Outcome: Build an LLM-Powered CRM Lead Qualifier Agent.

Week 2: RAG-Powered Knowledge Agents — I

Embeddings, chunking strategies, and vector store indexing
FAISS and Chroma with an offline indexing pipeline design
Indexing strategies: vector, summary, tree, keyword, and hybrid

Outcome: Build the SupportDesk-RAG Retrieval Layer for IT Support Knowledge Search.

Week 3: RAG-Powered Knowledge Agents — II

RAG pipeline architecture: retrieve, augment, and generate
Multi-turn RAG with conversation history and hallucination prevention
Retrieval and generation evaluation: Precision@K, groundedness, answer relevance

Outcome: Build a SupportDesk-RAG End-to-End Grounded IT Support Knowledge Assistant.

Week 4: Multi-Agent Systems

Role-based agent design: orchestrator, search, planner, and synthesizer
State, nodes, edges, and persistence using LangGraph checkpointers
Parallelization, subagent delegation, and context engineering

Outcome: Build a Multi-Agent AI Travel Planner.

Week 5: Conversational & Multimodal Agents

Voice agent architecture: cascaded STT, LLM, and TTS pipelines
Subgraphs, stateful persistence, and human-in-the-loop interrupts
Parallel agent routing and synthesizer design for unified output

Outcome: Build a Voice-Enabled Multimodal E-Commerce AI Assistant.

Week 6: Agent Communication Protocols

Finite state machines, structured message contracts, and MCP toolsets
Graph-based orchestration with LangGraph conditional edges and shared state
Networked agent deployment using A2A protocol and Google ADK

Outcome: Build a Real Estate Negotiation Multi-Agent Simulator.

Week 7: Hybrid Search & Retrieval

Sparse and dense vectors, SPLADE, and distance metrics
HNSW-based ANN search and inverted index design
Hybrid pipelines with Qdrant, BGE, and Reciprocal Rank Fusion

Outcome: Build a Hybrid Product Search Agent on the ESCI Dataset.

Week 8: Agent Observability, Evaluation, and Safety

LangSmith tracing, token cost logging, and hierarchical span analysis
LLM-as-judge evaluators, DeepEval metrics, and regression checks
Guardrails AI, PII redaction with Microsoft Presidio, and semantic safety enforcement

Outcome: Build a Production-Ready Fintech Support Agent.

Week 9: Fine-Tuning & Domain Adaptation

Fine-tuning landscape: LoRA, QLoRA, and PEFT methods
Dataset creation, quality auditing, and SFTTrainer on 4-bit quantized models
LLM-as-judge evaluation and Hugging Face Hub deployment

Outcome: Build a Domain-Specific Fine-Tuned Agent.

Weeks 10 & 11: Capstone — Enterprise Multi-Agent System

End-to-end multi-agent system with retrieval, APIs, and guardrails
LangGraph orchestration, Streamlit, AWS deployment, and cost monitoring
Safety enforcement, CI/CD, and production architecture review

Outcome: Build a Production-Grade Enterprise Multi-Agent System.

Agentic AI System Design and Interview Preparation for SWEs

Week 12: Agentic Research Systems: Planning, Tools & Guardrails

ReAct vs Plan-and-Execute vs Hybrid agent architectures
Tool integration: search APIs, browser tools, and retrieval systems
Guardrails: hallucination mitigation, source verification, and grounding

Outcome: Design reliable agentic research systems with guardrails.

Week 13: Agentic Text-to-SQL: Reliable Data Reasoning Systems

Schema retrieval, query generation, execution, and refinement pipelines
Guardrails: query validation, dangerous query prevention, and access limits
Evaluation: query correctness, semantic validation, and execution-based testing

Outcome: Build reliable Text-to-SQL agents with safe database reasoning.

Week 14: Multi-Agent Systems: Coordination & Shared Intelligence

Planner, researcher, executor, agent roles, and coordination strategies
Shared memory design, synchronization, and parallel agent execution
Reliability: cascading failures, agent disagreement, and infinite loop prevention

Outcome: Design coordinated multi-agent systems with shared intelligence.

Week 15: Self-Improving Agents: Evaluation & Verification Loops

Generate, test, and refine loops with iterative verification-based agents
Evaluation systems: test execution, static analysis, and candidate fix ranking
Productionization: sandboxed execution, compute limits, and rollback mechanisms

Outcome: Build self-improving agents with robust evaluation and verification loops.

Full-stack Engineering Interview Prep

Data Structures and Algorithms

Weeks 16–25

Scalable System Design

Weeks 26–31

Databases

Weeks 32–33 (Self-Paced)

API Design & Implementation

Week 34 (Self-Paced)

Cloud Infrastructure

Week 35 (Self-Paced)

Javascript and Web Development

Weeks 36–37 (Self-Paced)

UI System Design

Weeks 38–39 (Self-Paced)

Career workshops

Weeks 40 – 42

AI SDE-Specific Career Guidance & 1:1 Mentoring

Foundational Materials

Python Fundamentals Refresher

Evolution of GenAI

Hands-on with Generative AI Models

ML Foundations

Specialized Sessions

Laying the Groundwork for AI-Driven Development

Building Effective Prompts and Configuration-Driven Apps

Innovating with Multi-Agent Systems and Specialized Models

Harnessing LLM Frameworks for Real-World Development

From Development to Deployment: Scaling and Debugging AI Models

Domain interview prep is for Fullstack SWEs; Backend & Frontend tracks available too.

Detailed Curriculum: Applied Agentic AI for SWEs

Applied Agentic AI Core for SWEs

Week 0: Pre-Program Foundations

Colab setup, OpenAI APIs, and secure key management
Data handling with Pandas and strict system prompt design
End-to-end agent flow: input, reasoning, data access, response

Outcome: Build a Data-Grounded CSV FAQ Agent.

Week 1: Agentic AI Foundations & Reflex Agents

Evolution from rule-based systems to LLM-based agents
Agent anatomy: brain, planning, memory, tools, and ReAct loop
Prompt engineering, agentic design patterns, and tool use

Outcome: Build an LLM-Powered CRM Lead Qualifier Agent.

Week 2: RAG-Powered Knowledge Agents — I

Embeddings, chunking strategies, and vector store indexing
FAISS and Chroma with an offline indexing pipeline design
Indexing strategies: vector, summary, tree, keyword, and hybrid

Outcome: Build the SupportDesk-RAG Retrieval Layer for IT Support Knowledge Search.

Week 3: RAG-Powered Knowledge Agents — II

RAG pipeline architecture: retrieve, augment, and generate
Multi-turn RAG with conversation history and hallucination prevention
Retrieval and generation evaluation: Precision@K, groundedness, answer relevance

Outcome: Build a SupportDesk-RAG End-to-End Grounded IT Support Knowledge Assistant.

Week 4: Multi-Agent Systems

Role-based agent design: orchestrator, search, planner, and synthesizer
State, nodes, edges, and persistence using LangGraph checkpointers
Parallelization, subagent delegation, and context engineering

Outcome: Build a Multi-Agent AI Travel Planner.

Week 5: Conversational & Multimodal Agents

Voice agent architecture: cascaded STT, LLM, and TTS pipelines
Subgraphs, stateful persistence, and human-in-the-loop interrupts
Parallel agent routing and synthesizer design for unified output

Outcome: Build a Voice-Enabled Multimodal E-Commerce AI Assistant.

Week 6: Agent Communication Protocols

Finite state machines, structured message contracts, and MCP toolsets
Graph-based orchestration with LangGraph conditional edges and shared state
Networked agent deployment using A2A protocol and Google ADK

Outcome: Build a Real Estate Negotiation Multi-Agent Simulator.

Week 7: Hybrid Search & Retrieval

Sparse and dense vectors, SPLADE, and distance metrics
HNSW-based ANN search and inverted index design
Hybrid pipelines with Qdrant, BGE, and Reciprocal Rank Fusion

Outcome: Build a Hybrid Product Search Agent on the ESCI Dataset.

Week 8: Agent Observability, Evaluation, and Safety

LangSmith tracing, token cost logging, and hierarchical span analysis
LLM-as-judge evaluators, DeepEval metrics, and regression checks
Guardrails AI, PII redaction with Microsoft Presidio, and semantic safety enforcement

Outcome: Build a Production-Ready Fintech Support Agent.

Week 9: Fine-Tuning & Domain Adaptation

Fine-tuning landscape: LoRA, QLoRA, and PEFT methods
Dataset creation, quality auditing, and SFTTrainer on 4-bit quantized models
LLM-as-judge evaluation and Hugging Face Hub deployment

Outcome: Build a Domain-Specific Fine-Tuned Agent.

Weeks 10 & 11: Capstone — Enterprise Multi-Agent System

End-to-end multi-agent system with retrieval, APIs, and guardrails
LangGraph orchestration, Streamlit, AWS deployment, and cost monitoring
Safety enforcement, CI/CD, and production architecture review

Outcome: Build a Production-Grade Enterprise Multi-Agent System.

Agentic AI System Design and Interview Preparation for SWEs

Week 12: Agentic Research Systems: Planning, Tools & Guardrails

ReAct vs Plan-and-Execute vs Hybrid agent architectures
Tool integration: search APIs, browser tools, and retrieval systems
Guardrails: hallucination mitigation, source verification, and grounding

Outcome: Design reliable agentic research systems with guardrails.

Week 13: Agentic Text-to-SQL: Reliable Data Reasoning Systems

Schema retrieval, query generation, execution, and refinement pipelines
Guardrails: query validation, dangerous query prevention, and access limits
Evaluation: query correctness, semantic validation, and execution-based testing

Outcome: Build reliable Text-to-SQL agents with safe database reasoning.

Week 14: Multi-Agent Systems: Coordination & Shared Intelligence

Planner, researcher, executor, agent roles, and coordination strategies
Shared memory design, synchronization, and parallel agent execution
Reliability: cascading failures, agent disagreement, and infinite loop prevention

Outcome: Design coordinated multi-agent systems with shared intelligence.

Week 15: Self-Improving Agents: Evaluation & Verification Loops

Generate, test, and refine loops with iterative verification-based agents
Evaluation systems: test execution, static analysis, and candidate fix ranking
Productionization: sandboxed execution, compute limits, and rollback mechanisms

Outcome: Build self-improving agents with robust evaluation and verification loops.

Full-stack Engineering Interview Prep

Data Structures and Algorithms

Weeks 16–25

Scalable System Design

Weeks 26–31

Databases

Weeks 32–33 (Self-Paced)

API Design & Implementation

Week 34 (Self-Paced)

Cloud Infrastructure

Week 35 (Self-Paced)

Javascript and Web Development

Weeks 36–37 (Self-Paced)

UI System Design

Weeks 38–39 (Self-Paced)

Career workshops

Weeks 40 – 42

Domain interview prep is for Fullstack SWEs; Backend & Frontend tracks available too.

Live Guided Projects

CRM Lead Qualifier Agent

Build an LLM-powered agent that automates sales lead qualification using function calling as a reasoning engine. The agent uses tools like domain lookup, CRM history check, and lead scoring, powered by a think-act-observe loop to make real-world sales decisions.

SupportDesk-RAG

Build a production-ready RAG system for IT support ticket troubleshooting using LangChain, LlamaIndex, FAISS, and Chroma. The system covers five indexing approaches, a complete LCEL pipeline, anti-hallucination safeguards, two-layer retrieval and generation evaluation, and an agentic RAG extension with multi-step conversation memory.

Multi-Agent Travel Planner

Build a multi-agent travel planning workflow using an Orchestrator → Search → Itinerary Planner → Synthesizer pattern with LangGraph and LangChain. Specialized agents collaborate to search flights and hotels, generate itineraries, and synthesize unified recommendations across routing, parallelization, and shared state.

AxiomCart — Voice-Enabled Shopping Assistant

Build a stateful voice-enabled multi-agent shopping assistant using LangGraph StateGraph with an Orchestrator → Product Agent + Support Agent → Synthesizer pattern. Integrates RAG-powered product discovery, human-in-the-loop interrupts, parallel agent dispatch, MemorySaver checkpointing, and a Whisper + OpenAI TTS voice pipeline.

Real Estate Negotiation Simulator

Build a buyer-seller negotiation system where agents communicate using typed Pydantic message schemas. Progress from a naive, broken implementation to a robust architecture with FSM terminal states, MCP-grounded pricing tools, LangGraph workflow routing, and true A2A protocol transport via Google ADK.

Hybrid Product Search Agent

Build a hybrid product search system over Amazon’s ESCI dataset, combining SPLADE sparse embeddings and BGE-Large dense embeddings indexed in Qdrant. Merge parallel retrieval results via Reciprocal Rank Fusion to return precise, context-aware product matches across lexical and semantic signals.

Production-Ready Fintech Support Agent

Build a multi-agent FinTech customer support system with a supervisor and specialist agents covering policy RAG, account lookup, and escalation. Hardened with LangSmith tracing, DeepEval metrics, Guardrails AI validators, Microsoft Presidio PII redaction, and tiktoken-powered cost-per-query optimization.

Domain-Specific Fine-Tuned Agent

Build a domain-adapted Healthcare Q&A agent by fine-tuning a 4-bit quantized Qwen2.5-1.5B-Instruct model with QLoRA. Deploy the LoRA adapter to Hugging Face Hub and evaluate side-by-side against the base model in LangSmith using LLM-as-judge scoring across accuracy, helpfulness, and safety.

Projects are subject to change as per industry inputs.

Live Guided Projects

CRM Lead Qualifier Agent

Build an LLM-powered agent that automates sales lead qualification using function calling as a reasoning engine. The agent uses tools like domain lookup, CRM history check, and lead scoring, powered by a think-act-observe loop to make real-world sales decisions.

SupportDesk-RAG

Build a production-ready RAG system for IT support ticket troubleshooting using LangChain, LlamaIndex, FAISS, and Chroma. The system covers five indexing approaches, a complete LCEL pipeline, anti-hallucination safeguards, two-layer retrieval and generation evaluation, and an agentic RAG extension with multi-step conversation memory.

Multi-Agent Travel Planner

Build a multi-agent travel planning workflow using an Orchestrator → Search → Itinerary Planner → Synthesizer pattern with LangGraph and LangChain. Specialized agents collaborate to search flights and hotels, generate itineraries, and synthesize unified recommendations across routing, parallelization, and shared state.

AxiomCart — Voice-Enabled Shopping Assistant

Build a stateful voice-enabled multi-agent shopping assistant using LangGraph StateGraph with an Orchestrator → Product Agent + Support Agent → Synthesizer pattern. Integrates RAG-powered product discovery, human-in-the-loop interrupts, parallel agent dispatch, MemorySaver checkpointing, and a Whisper + OpenAI TTS voice pipeline.

Real Estate Negotiation Simulator

Build a buyer-seller negotiation system where agents communicate using typed Pydantic message schemas. Progress from a naive, broken implementation to a robust architecture with FSM terminal states, MCP-grounded pricing tools, LangGraph workflow routing, and true A2A protocol transport via Google ADK.

Hybrid Product Search Agent

Build a hybrid product search system over Amazon’s ESCI dataset, combining SPLADE sparse embeddings and BGE-Large dense embeddings indexed in Qdrant. Merge parallel retrieval results via Reciprocal Rank Fusion to return precise, context-aware product matches across lexical and semantic signals.

Production-Ready Fintech Support Agent

Build a multi-agent FinTech customer support system with a supervisor and specialist agents covering policy RAG, account lookup, and escalation. Hardened with LangSmith tracing, DeepEval metrics, Guardrails AI validators, Microsoft Presidio PII redaction, and tiktoken-powered cost-per-query optimization.

Domain-Specific Fine-Tuned Agent

Build a domain-adapted Healthcare Q&A agent by fine-tuning a 4-bit quantized Qwen2.5-1.5B-Instruct model with QLoRA. Deploy the LoRA adapter to Hugging Face Hub and evaluate side-by-side against the base model in LangSmith using LLM-as-judge scoring across accuracy, helpfulness, and safety.

Projects are subject to change as per industry inputs.

Production-Grade Capstone Projects for Agentic AI Engineers

Capstones stay aligned with industry needs. Pick from 10 production-grade projects or build your own system.

AI Finance Assistant

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.

AI Content Marketing Assistant

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.

AI Call Center Assistant

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.

AI-Powered Email Assistant

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.

AI-Powered DevOps Assistant

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.

AI-Powered Patient Assistant (Healthcare)

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.

AI-Powered Security Auditor

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.

AI-Driven Legal Document Analyzer

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.

AI Supply Chain Optimization Assistant

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.

Automated Code Reviewer/Pull Request Reviewer Bot Powered by LLMs

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.

Resume/ATS scoring assistant

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.

BYOP [Bring Your Own Project]

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.

Production-Grade Capstone Projects for Agentic AI Engineers

AI Finance Assistant

AI Content Marketing Assistant

AI Call Center Assistant

AI-Powered Email Assistant

AI-Powered DevOps Assistant

AI-Powered Patient Assistant (Healthcare)

AI-Powered Security Auditor

AI-Driven Legal Document Analyzer

AI Supply Chain Optimization Assistant

Automated Code Reviewer/Pull Request Reviewer Bot Powered by LLMs

Resume/ATS scoring assistant

BYOP [Bring Your Own Project]

Projects are subject to change as per industry inputs. Choose from one of 10 Capstone Projects.

+ Instructors to Train You

Get mentored by AI/ML leaders who are driving Agentic AI innovation at top global companies.

Jigyashu Keshri

Senior Software Engineer

Anshaj Khare

Software Engineer

Harshit Laddha

Senior Software Engineer

Prateel Singhal

Data Engineer

Shashank Gupta

Senior Applied Scientist

Ishaan Sutaria

Head of Engineering

Prajwal Rao

Sr Applied Data Scientist

Learn more about the course & pricing

The IK Experience: What Our Alumni Are Saying

Akshay Lodha

Data Engineer

Placed at:

The experience with Interview Kickstart was phenomenal. It was worth it. After so many years of interviewing, Interview Kickstart helped me a lot in orienting myself and getting into the rhythm. Had a transition from Goldman Sachs to Facebook.

Souptik Sen

Data Engineer

Placed at:

The classes like the BST and DP live classes were amazing. Did the accelerated program and it was very flexible for me to have the recordings of each class. Thanks to IK, I was able to get into Snowflake!

Kushal L

Software Engineer

Placed at:

IK provides a nice, structured way to prepare for interviews while having a full-time job. Mock interviews helped me get better and the problem sets alleviated the need for me to source problems externally.

Megha Gaikwad

Software Development Manager III

Placed at:

I didn’t just want to stay relevant in AI. I wanted to lead from the front. IK gave me the structure, mentorship, and confidence to connect vision with execution and step into a CBO role. It turned my ambition into a career leap.

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FAQs

What is this program and who is it designed for?

This is a comprehensive program built for software engineers and senior technical professionals who want to master Agentic AI and prepare for Tier 1 engineering roles. It is designed for backend, full stack, data, and platform engineers, as well as technically strong PMs, TPMs, and EMs who work closely with engineering systems.

How is this program different from other Agentic AI or GenAI courses?

Most programs stop at tools or demos. This program goes further by teaching how to design, evaluate, operate, and defend production-grade agentic systems. It uniquely combines Applied Agentic AI, Agentic AI interview preparation, and domain-level interview preparation in one end-to-end path.

What exactly is Agentic AI and why are companies prioritizing it now?

Agentic AI refers to systems that reason, plan, call tools, coordinate with other agents, and operate within real workflows. Companies are moving beyond chat interfaces toward AI systems that automate decisions and actions. Engineers are now expected to build and manage these systems reliably.

Why is learning Agentic AI critical for Tier 1 engineering roles in 2026?

Tier 1 companies increasingly evaluate engineers on their ability to design AI-powered systems, reason about tradeoffs, handle failures, and control cost and risk. Agentic AI skills are becoming part of core system design and architecture expectations.

What roles can this program help me prepare for?

The program prepares learners for roles such as Backend Engineer, Full Stack Engineer, AI Engineer, Platform Engineer, Senior Software Engineer, Technical Lead, and AI-focused PM or TPM roles where system-level reasoning is required.

Do I need prior AI or machine learning experience to join?

No prior AI or machine learning experience is required. The program starts from foundational concepts and builds up. However, it assumes strong software engineering fundamentals.

What programming background is expected before starting the course?

You should be comfortable with coding, APIs, and basic system concepts. Experience with backend or distributed systems is helpful. This is not a beginner programming course.

How is the program structured across Agentic AI, Agentic AI interview prep, and domain interview prep?

The program has three integrated layers. First, you build applied Agentic AI systems. Second, you learn how to reason about and explain those systems in interviews. Third, you prepare for domain interviews covering data structures, system design, revisit your core domain topics like backend engineering, and full stack.

What topics are covered in the Applied Agentic AI curriculum?

You cover agent foundations, RAG systems, multi-agent orchestration, conversational and multimodal agents, structured communication protocols, domain-specific agents, evaluation, safety, cost optimization, fine tuning, and production deployment.

What is included in the Agentic AI interview preparation portion?

You learn how to choose agentic versus deterministic approaches, explain design patterns, define tool contracts, reason about orchestration and memory, handle failure modes, and defend decisions through real interview-style case questions.

What does domain-level interview preparation cover?

Domain preparation includes data structures and algorithms, system design principles, core domain topics. It also focuses on applying these concepts to build scalable, reliable systems and solutions, aligned with Tier-1 company interview expectations.

How does this course help me think in systems, not just tools or prompts?

Every concept is taught through architecture, tradeoffs, failure analysis, and evaluation. You learn when not to use agents, how to simplify designs, and how to balance quality, cost, latency, and risk in real systems.

What tools, frameworks, and technologies will I work with?

You will work with Python, LangChain, LangGraph, CrewAI, OpenAI APIs, Hugging Face tools, FAISS, Chroma, FastAPI, Streamlit, LangSmith, TruLens, Docker, and production monitoring concepts.

What are Live Guided Projects and how do they work?

Live Guided Projects are instructor-led, code-along builds where you learn how to design and implement systems step by step. They focus on learning the correct mental model without overwhelming you.

How are Capstone Projects different from Live Guided Projects?

Capstone Projects are learner-driven and enterprise-scale. You apply everything you have learned, receive structured feedback, iterate on your design, and present your system like a real engineering review.

What kind of real-world systems will I build during the program?

You will build RAG-based knowledge assistants, multi-agent research systems, conversational agents with memory and voice, negotiation simulators, decision support systems, production-ready support agents, and a full enterprise-grade multi-agent capstone.

Will I learn evaluation, safety, observability, and cost optimization for AI systems?

Yes. Evaluation, guardrails, observability, logging, cost tracking, and optimization are core parts of the curriculum. You learn how to operate AI systems responsibly in production.

How does this program prepare me for real Tier 1 interviews beyond coding practice?

The program focuses heavily on reasoning, communication, and tradeoff discussion. You practice explaining architectures, handling follow-up questions, and defending decisions the way Tier 1 interviewers expect.

What kind of mock interviews and feedback do I receive?

You receive mock interviews with senior engineers and hiring managers, along with detailed feedback on clarity, correctness, structure, and decision-making.

Who teaches this program and what is their real-world experience?

The instructors are AI/ML practitioners from FAANG and other Tier 1 companies who bring practical, production-level experience to the classroom.

Can PMs, TPMs, or EMs take this program without deep coding backgrounds?

Yes, as long as you are technically strong and comfortable with system concepts. The program emphasizes reasoning, architecture, and decision-making, not just writing code.

What career support is included beyond the core curriculum?

You receive resume and LinkedIn optimization, behavioral interview preparation, offer negotiation guidance, and extended support through mock interviews and expert sessions.

What outcomes can I realistically expect after completing this program?

You graduate with a strong portfolio of production-style agentic systems, confidence in AI system design, and readiness for Tier 1 interviews that test both engineering depth and AI judgment. Alumni report an average compensation of ₹60 LPA, a 2x-5x ROI on course investment, and successful transitions into top-tier companies with AI-focused roles.

Do I get interview prep support?

Yes. You get up to 15 mock interview sessions with hiring managers and senior technical experts from FAANG+ companies, designed to closely simulate real interview scenarios.

This includes 5 Agentic AI–focused mock interviews covering agentic system design, architecture trade-offs, evaluation, and production readiness, along with 10 domain-level mock interviews tailored to your role (SWE, PM, TPM, or EM), covering system design, coding, and role-specific rounds.

Is there a coding prerequisite?

For Software Engineering track, relevant coding experience is required. For participants in non-software programs, coding experience is good-to-have, but not mandatory.

How does this course help in my specific domain?

Software Engineers & AI Engineers:

Learn to build and deploy scalable AI-driven backend systems

Get hands-on with LangChain, CrewAI, and AutoGen

Master orchestration and real-time inference in production