Transformative GenAI Domain-specific Track
Flagship Machine Learning Program
PwC India x IK Agentic AI Career Accelerator
Interview Preparation Domain-specific Track
Learn from accomplished MAANG+ engineers to build, deploy, and defend enterprise agentic AI systems in Tier-1 interviews.
Program fully updated for 2026 with applied agentic AI training, agentic-AI interview prep, and domain-focused system design. Everything required to clear modern Tier-1 tech interviews.
Live & Flexible Online Classes | Agentic AI and Domain-specific Interview Prep | 1:1 Career Support
Best Suited for:
Agentic AI is now a Tier-1 Requirement, and is no longer optional in modern tech roles.
We analyzed 15,000+ job descriptions across MAANG and Tier-1 companies, and 35% already require Generative or Agentic AI skills. That number is accelerating fast.
This program combines applied Agentic AI, Agentic AI interview prep and domain-focused interview preparation in a single path.
40-Week Comprehensive Program
Time commitment: 5-7 hours per week
130+ Total Learning Hours
Weekly commitment: 5-7 hours (fits your schedule)
Build 9+ Production-Ready Systems:
Learn from engineers who build agentic AI at Meta, Google, Amazon, Microsoft, and top AI-first companies.
Learn how modern Agentic AI systems are designed, built, and evaluated and how to confidently present that experience in Tier-1 interviews.
Up to 20 mock interviews with MAANG level hiring managers and senior engineers
Master what most courses skip: evaluation frameworks, observability, safety guardrails, cost optimization, and enterprise deployment, the skills Tier-1 teams actually need.
Upto 40-week curriculum with just 5-7 hours/week. All sessions are delivered live, and also recorded for later viewing. Flexible schedule designed for working professionals.
A 2026-ready curriculum covering agentic AI system design, real-world deployment, evaluation, safety, and cost control used by Tier-1 tech teams.
Learn from engineers actively building agentic systems at Meta, Google, Amazon. They bring real production challenges, solutions, and war stories to every class.
Go beyond tutorials. Build 9+ real agentic AI systems through live projects and enterprise-grade capstones that mirror how these systems work in production.
Focus on tailored AI applications and leverage the power of Agents relevant to PMs, TPMs, EMs, and SDEs. Topics include AI-powered product execution, LLMOps, technical feasibility, and managing AI teams.
Average rating of 4.75 for our Applied Agentic AI program, learners love our structured and hands-on approach.
Build real agentic AI systems step by step with expert guidance.
Build your first LLM-powered agent to understand how agents differ from chatbots, how LLMs act as reasoning engines, and how tools and control logic shape agent behavior.
Production-ready RAG system for IT support ticket troubleshooting. End-to-end pipeline covering embeddings, chunking, indexing, and vector search with anti-hallucination safeguards and retrieval evaluation.
Build a multi-agent travel planner using an orchestrated search-planner-synthesizer flow with specialized agents for end-to-end itinerary generation.
A voice-enabled, multimodal customer support system with sales and product discovery agents, HITL interrupts, and a synthesized audio response pipeline.
Build a buyer-seller negotiation system using Pydantic schemas, FSM control, MCP tools, LangGraph routing, and A2A via Google ADK for reliable agent interactions.
Build a hybrid search system combining SPLADE and BGE embeddings with Qdrant and RRF for accurate product retrieval.
Customer support agent integrating RAG, safety guardrails, evaluation pipelines, and cost tracking dashboards, demonstrating responsible, efficient operation in production environments.
Domain-adapted agent fine-tuned for a vertical domain. Compares performance against prompting and RAG baselines and evaluates cost-benefit trade-offs to inform deployment decisions.
A LangChain agent that generates personalized welcome emails from SaaS signup records with ICP fit scoring, evaluated with a golden dataset via DeepEval GEval and LangSmith online tracing.
Build real agentic AI systems step by step with expert guidance.
Build your first LLM-powered agent to understand how agents differ from chatbots, how LLMs act as reasoning engines, and how tools and control logic shape agent behavior.
Production-ready RAG system for IT support ticket troubleshooting. End-to-end pipeline covering embeddings, chunking, indexing, and vector search with anti-hallucination safeguards and retrieval evaluation.
Build a multi-agent travel planner using an orchestrated search-planner-synthesizer flow with specialized agents for end-to-end itinerary generation.
A voice-enabled, multimodal customer support system with sales and product discovery agents, HITL interrupts, and a synthesized audio response pipeline.
Build a buyer-seller negotiation system using Pydantic schemas, FSM control, MCP tools, LangGraph routing, and A2A via Google ADK for reliable agent interactions.
Build a hybrid search system combining SPLADE and BGE embeddings with Qdrant and RRF for accurate product retrieval.
Customer support agent integrating RAG, safety guardrails, evaluation pipelines, and cost tracking dashboards, demonstrating responsible, efficient operation in production environments.
Domain-adapted agent fine-tuned for a vertical domain. Compares performance against prompting and RAG baselines and evaluates cost-benefit trade-offs to inform deployment decisions.
A LangChain agent that generates personalized welcome emails from SaaS signup records with ICP fit scoring, evaluated with a golden dataset via DeepEval GEval and LangSmith online tracing.
Projects are subject to change as per industry inputs. Choose from one of 4 Capstone Projects.
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
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. In this project, leverage Python, TensorFlow, XGBoost, LangChain, OpenAI API, SQL/NoSQL databases, and visualization tools like Streamlit.
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.
Call Center Summarization App Powered by LLMs
Enhance call center operations with an AI-powered summarization bot that leverages Large Language Models (LLMs) to generate concise summaries of customer interactions. This tool provides quick overviews, improving decision-making and customer service efficiency. The bot automates manual summary writing, ensuring consistent and accurate records. Integrate with GPT-4, Cohere, or Hugging Face Transformers for superior NLP capabilities. Build the interface with React or Streamlit, and deploy using Docker and AWS for seamless operation.
Email Generator App
Streamline email communication with an AI-powered Email Generator App that leverages Large Language Models (LLMs) to generate professional and contextually accurate email drafts. The app provides quick, reliable suggestions based on user inputs, ensuring high accuracy and relevance. It supports customization and personalization, enhancing the efficiency of email management. Integrate with models like GPT-4, Cohere, or Hugging Face Transformers for superior performance. Build the interface with React or Streamlit, and deploy the application using Docker and AWS for seamless operation.
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 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.
Autonomous ETL/ELT Agent for DevOps-Driven Data Engineering
Build an intelligent multi-agent system that automates end-to-end data pipeline development from requirements to production deployment. A Story-Parser agent extracts intents from natural language, a Codegen agent builds Spark/Databricks pipelines, a QA agent auto-writes tests, a DevOps agent raises pull requests, and a Deployer/Orchestrator schedules runs via Airflow/ADF. The system uses GPT/Hugging Face for requirement parsing, LangChain/Semantic Kernel for prompt-to-code translation, and Great Expectations/Delta Live for data quality enforcement. Built-in guardrails include schema validation, NULL checks, and business-rule tests via ScalaTest. Deploy cloud-ready outputs with CI/CD hooks that commit code, open PRs with test artifacts, and deploy JARs/notebooks to Databricks/Azure Synapse, supporting Parquet/CSV/Delta formats on ADLS/S3.
Intelligent Data Quality System
Create a comprehensive multi-agent data quality copilot that transforms DQ management from reactive firefighting to proactive intelligence. A Query Agent converts natural language to SQL, a Data Quality Agent evaluates completeness, consistency, timeliness, accuracy, and relevance, while a Report Agent generates HTML dashboards to surface issues rapidly. Plug-and-play connectors scan databases, data lakes, APIs, and streams with auto-profiling capabilities that detect structure, distributions, anomalies, and outliers at scale. The system delivers actionable insights with human-readable explanations and recommended fixes, extensible with an Auto-Fixer agent for closed-loop remediation. The outcome is a smart, end-to-end data quality assistant that reduces manual effort, boosts data trust, and democratizes DQ for business users.
Industry-Wide Financial Trend Analysis
Develop an agentic market intelligence pipeline that delivers always-on sector visibility through automated real-time analysis. The system auto-ingests live stock data, industry news, social sentiment, and optional macroeconomic signals to build comprehensive views of any sector. AI-powered analysis correlates sentiment with price movements and volatility to detect momentum shifts, surface risks and opportunities, and identify industry leaders versus laggards. Users can ask natural language questions like “What’s the trend in renewable energy?” and receive concise outlooks compiled from live data and NLP analysis. The system generates investor-ready HTML/PDF dashboards and summaries for short- and mid-term industry outlooks, complete with key drivers and actionable insights.
Automated Data Insights Generator
Build a chat‑based analytics copilot that lets non‑technical users ask questions in natural language and receive high‑quality textual and visual insights. You’ll implement a CSV‑to‑SQL ingestion pipeline that creates the right schemas/tables and loads datasets into a relational store, then wire up LangChain for streamlined database access and NL→SQL using the SQLDatabaseToolkit and prompt templates. The front end is a Streamlit app with conversational memory for iterative exploration, producing real‑time answers and charts. Extension tracks include adding support for MongoDB/Spark, scheduling recurring insight runs, and exporting outputs to PowerBI, Tableau, or Google Data Studio.
Multi-Agent System for Engineering Productivity & Burnout Monitoring
Build a comprehensive engineering team health system using CrewAI to improve productivity while safeguarding well-being. You’ll create a workload analysis agent that tracks sprint metrics and code velocity, design a burnout detection agent that identifies risk patterns in work hours and meeting loads, and implement an optimization agent that recommends balanced task distribution. Working with Jira API and Slack integration, you’ll gain experience creating AI systems that enhance team efficiency while prioritizing engineer wellness.
Multi-Agent AI System for Engineering Roadmap & Strategy Planning
Craft an intelligent engineering strategy system using LangGraph and OpenAI that continuously evolves your technical direction. You’ll implement a trend analysis agent that monitors tech blogs, conferences, and competitor repositories, develop an evaluation agent that assesses emerging frameworks against your needs, and build a strategic planning agent that recommends practical roadmap adjustments based on team capacity. Through real-time web scraping and AI analysis, you’ll learn to create systems that keep engineering organizations ahead of industry shifts while maintaining realistic implementation plans.
AI Agent for Cloud Cost Optimization in Engineering Workloads
Create a cloud cost management system using LangChain to monitor AI/ML expenses. You’ll build agents that track compute usage across AWS/GCP/Azure, recommend cost-effective configurations like serverless solutions, and alert teams to unexpected spikes. By integrating with AWS Cost Explorer API and Terraform, you’ll learn to automate financial oversight while balancing performance with budget constraints.
AI-Powered Stakeholder Management Bot
Develop an AI chatbot that helps TPMs track stakeholder interactions. The bot summarizes emails, meeting transcripts, and sentiment trends. It will alert TPMs when a key stakeholder engagement score is declining.
Multi-Agent AI System for Program Risk Management
Design an intelligent risk management system for AI/ML initiatives using LangChain and CrewAI. You’ll develop a risk assessment agent that analyzes project documentation and historical risk data, create a dependency tracker that identifies cross-team bottlenecks, and implement a mitigation planning agent that generates targeted risk reduction strategies. By leveraging OpenAI function calling and RAG-based retrieval, you’ll learn to build proactive systems that anticipate problems before they impact project timelines or outcomes.
AI-Driven Engineering Capacity & Resource Allocation Agent
Build an AI-powered system to automate workload balancing and engineering resource forecasting. Using LangChain and CrewAI for multi-agent collaboration, the system includes a workload analysis agent that scans Jira and GitHub activity, a resource planning agent that predicts developer bandwidth and recommends reallocation, and a capacity planning agent that aligns hiring needs with sprint planning—leveraging OpenAI embeddings to analyze and optimize developer workloads.
AI-Powered Feature Prioritization Tool
Build an AI agent that evaluates feature requests based on user impact, development effort, and business alignment, then automatically prioritizes them. The agent will integrate with Jira or Asana to create tickets, streamlining the product development pipeline. You’ll use LLMs to perform text-based analysis of customer feedback and market trends, enabling data-driven, scalable feature prioritization.
Customer Sentiment Analysis & Roadmap Alignment
Create an AI agent that ingests customer complaints, app reviews, and support tickets to identify key product insights. Using AI-based classification and clustering, the agent will group feedback into common themes and auto-generate reports that align top complaints with upcoming roadmap items. This enables proactive product planning and faster response to customer pain points.
AI-Driven Competitive Landscape Analysis
Build an AI-powered research assistant that scrapes competitor websites, product releases, and industry news to stay ahead of market shifts. Using retrieval-augmented generation (RAG), the agent generates concise, actionable reports highlighting competitors’ moves, pricing strategies, feature gaps, and emerging market trends—helping product and strategy teams make informed decisions faster.
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 Project Management & Task Automation
Create a multi-agent project management system that combines LangGraph, Jira API, OpenAI, and Zapier to streamline workflow. You’ll develop three specialized agents: one for intelligent task prioritization based on urgency and dependencies, another for optimizing resource allocation across teams, and a third for monitoring KPIs to predict potential delays. This automated system will enhance planning efficiency, execution coordination, and real-time performance tracking.
AI-Powered Knowledge Management & Retrieval System
Develop an AI research assistant by constructing a multi-agent system that streamlines information discovery for professionals. You’ll engineer a document ingestion agent that processes PDFs, reports, and books into searchable data, implement a semantic search agent for precise information retrieval, and create a summarization agent that translates complex findings into clear explanations. Through hands-on experience with RAG architecture, OpenAI, and vector databases like Pinecone or Weaviate, you’ll gain practical skills in building intelligent knowledge systems.
Projects are subject to change as per industry inputs. This is a comprehensive list of Capstone Projects—you may work on 1 or more.
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
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. In this project, leverage Python, TensorFlow, XGBoost, LangChain, OpenAI API, SQL/NoSQL databases, and visualization tools like Streamlit.
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.
Call Center Summarization App Powered by LLMs
Enhance call center operations with an AI-powered summarization bot that leverages Large Language Models (LLMs) to generate concise summaries of customer interactions. This tool provides quick overviews, improving decision-making and customer service efficiency. The bot automates manual summary writing, ensuring consistent and accurate records. Integrate with GPT-4, Cohere, or Hugging Face Transformers for superior NLP capabilities. Build the interface with React or Streamlit, and deploy using Docker and AWS for seamless operation.
Email Generator App
Streamline email communication with an AI-powered Email Generator App that leverages Large Language Models (LLMs) to generate professional and contextually accurate email drafts. The app provides quick, reliable suggestions based on user inputs, ensuring high accuracy and relevance. It supports customization and personalization, enhancing the efficiency of email management. Integrate with models like GPT-4, Cohere, or Hugging Face Transformers for superior performance. Build the interface with React or Streamlit, and deploy the application using Docker and AWS for seamless operation.
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 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.
Autonomous ETL/ELT Agent for DevOps-Driven Data Engineering
Build an intelligent multi-agent system that automates end-to-end data pipeline development from requirements to production deployment. A Story-Parser agent extracts intents from natural language, a Codegen agent builds Spark/Databricks pipelines, a QA agent auto-writes tests, a DevOps agent raises pull requests, and a Deployer/Orchestrator schedules runs via Airflow/ADF. The system uses GPT/Hugging Face for requirement parsing, LangChain/Semantic Kernel for prompt-to-code translation, and Great Expectations/Delta Live for data quality enforcement. Built-in guardrails include schema validation, NULL checks, and business-rule tests via ScalaTest. Deploy cloud-ready outputs with CI/CD hooks that commit code, open PRs with test artifacts, and deploy JARs/notebooks to Databricks/Azure Synapse, supporting Parquet/CSV/Delta formats on ADLS/S3.
Intelligent Data Quality System
Create a comprehensive multi-agent data quality copilot that transforms DQ management from reactive firefighting to proactive intelligence. A Query Agent converts natural language to SQL, a Data Quality Agent evaluates completeness, consistency, timeliness, accuracy, and relevance, while a Report Agent generates HTML dashboards to surface issues rapidly. Plug-and-play connectors scan databases, data lakes, APIs, and streams with auto-profiling capabilities that detect structure, distributions, anomalies, and outliers at scale. The system delivers actionable insights with human-readable explanations and recommended fixes, extensible with an Auto-Fixer agent for closed-loop remediation. The outcome is a smart, end-to-end data quality assistant that reduces manual effort, boosts data trust, and democratizes DQ for business users.
Industry-Wide Financial Trend Analysis
Develop an agentic market intelligence pipeline that delivers always-on sector visibility through automated real-time analysis. The system auto-ingests live stock data, industry news, social sentiment, and optional macroeconomic signals to build comprehensive views of any sector. AI-powered analysis correlates sentiment with price movements and volatility to detect momentum shifts, surface risks and opportunities, and identify industry leaders versus laggards. Users can ask natural language questions like “What’s the trend in renewable energy?” and receive concise outlooks compiled from live data and NLP analysis. The system generates investor-ready HTML/PDF dashboards and summaries for short- and mid-term industry outlooks, complete with key drivers and actionable insights.
Automated Data Insights Generator
Build a chat‑based analytics copilot that lets non‑technical users ask questions in natural language and receive high‑quality textual and visual insights. You’ll implement a CSV‑to‑SQL ingestion pipeline that creates the right schemas/tables and loads datasets into a relational store, then wire up LangChain for streamlined database access and NL→SQL using the SQLDatabaseToolkit and prompt templates. The front end is a Streamlit app with conversational memory for iterative exploration, producing real‑time answers and charts. Extension tracks include adding support for MongoDB/Spark, scheduling recurring insight runs, and exporting outputs to PowerBI, Tableau, or Google Data Studio.
Multi-Agent System for Engineering Productivity & Burnout Monitoring
Build a comprehensive engineering team health system using CrewAI to improve productivity while safeguarding well-being. You’ll create a workload analysis agent that tracks sprint metrics and code velocity, design a burnout detection agent that identifies risk patterns in work hours and meeting loads, and implement an optimization agent that recommends balanced task distribution. Working with Jira API and Slack integration, you’ll gain experience creating AI systems that enhance team efficiency while prioritizing engineer wellness.
Multi-Agent AI System for Engineering Roadmap & Strategy Planning
Craft an intelligent engineering strategy system using LangGraph and OpenAI that continuously evolves your technical direction. You’ll implement a trend analysis agent that monitors tech blogs, conferences, and competitor repositories, develop an evaluation agent that assesses emerging frameworks against your needs, and build a strategic planning agent that recommends practical roadmap adjustments based on team capacity. Through real-time web scraping and AI analysis, you’ll learn to create systems that keep engineering organizations ahead of industry shifts while maintaining realistic implementation plans.
AI Agent for Cloud Cost Optimization in Engineering Workloads
Create a cloud cost management system using LangChain to monitor AI/ML expenses. You’ll build agents that track compute usage across AWS/GCP/Azure, recommend cost-effective configurations like serverless solutions, and alert teams to unexpected spikes. By integrating with AWS Cost Explorer API and Terraform, you’ll learn to automate financial oversight while balancing performance with budget constraints.
AI-Powered Stakeholder Management Bot
Develop an AI chatbot that helps TPMs track stakeholder interactions. The bot summarizes emails, meeting transcripts, and sentiment trends. It will alert TPMs when a key stakeholder engagement score is declining.
Multi-Agent AI System for Program Risk Management
Design an intelligent risk management system for AI/ML initiatives using LangChain and CrewAI. You’ll develop a risk assessment agent that analyzes project documentation and historical risk data, create a dependency tracker that identifies cross-team bottlenecks, and implement a mitigation planning agent that generates targeted risk reduction strategies. By leveraging OpenAI function calling and RAG-based retrieval, you’ll learn to build proactive systems that anticipate problems before they impact project timelines or outcomes.
AI-Driven Engineering Capacity & Resource Allocation Agent
Build an AI-powered system to automate workload balancing and engineering resource forecasting. Using LangChain and CrewAI for multi-agent collaboration, the system includes a workload analysis agent that scans Jira and GitHub activity, a resource planning agent that predicts developer bandwidth and recommends reallocation, and a capacity planning agent that aligns hiring needs with sprint planning—leveraging OpenAI embeddings to analyze and optimize developer workloads.
AI-Powered Feature Prioritization Tool
Build an AI agent that evaluates feature requests based on user impact, development effort, and business alignment, then automatically prioritizes them. The agent will integrate with Jira or Asana to create tickets, streamlining the product development pipeline. You’ll use LLMs to perform text-based analysis of customer feedback and market trends, enabling data-driven, scalable feature prioritization.
Customer Sentiment Analysis & Roadmap Alignment
Create an AI agent that ingests customer complaints, app reviews, and support tickets to identify key product insights. Using AI-based classification and clustering, the agent will group feedback into common themes and auto-generate reports that align top complaints with upcoming roadmap items. This enables proactive product planning and faster response to customer pain points.
AI-Driven Competitive Landscape Analysis
Build an AI-powered research assistant that scrapes competitor websites, product releases, and industry news to stay ahead of market shifts. Using retrieval-augmented generation (RAG), the agent generates concise, actionable reports highlighting competitors’ moves, pricing strategies, feature gaps, and emerging market trends—helping product and strategy teams make informed decisions faster.
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 Project Management & Task Automation
Create a multi-agent project management system that combines LangGraph, Jira API, OpenAI, and Zapier to streamline workflow. You’ll develop three specialized agents: one for intelligent task prioritization based on urgency and dependencies, another for optimizing resource allocation across teams, and a third for monitoring KPIs to predict potential delays. This automated system will enhance planning efficiency, execution coordination, and real-time performance tracking.
AI-Powered Knowledge Management & Retrieval System
Develop an AI research assistant by constructing a multi-agent system that streamlines information discovery for professionals. You’ll engineer a document ingestion agent that processes PDFs, reports, and books into searchable data, implement a semantic search agent for precise information retrieval, and create a summarization agent that translates complex findings into clear explanations. Through hands-on experience with RAG architecture, OpenAI, and vector databases like Pinecone or Weaviate, you’ll gain practical skills in building intelligent knowledge systems.
+ Instructors to Train You
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.
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!
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.
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.
Learn more about Interview Kickstart and the EdgeUp Program by joining the free pre-enrollment webinar.
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
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Join 25,000+ tech professionals who’ve accelerated their careers with cutting-edge AI skills
Join 25,000+ tech professionals who’ve accelerated their careers with cutting-edge AI skills
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Time Zone: Asia/Kolkata
Hands-on AI/ML learning + interview prep to help you win
Time Zone: Asia/Kolkata
Hands-on AI/ML learning + interview prep to help you win
Explore your personalized path to AI/ML/Gen AI success
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