Transformative GenAI Domain-specific Track
Flagship Machine Learning Program
PwC India x IK Agentic AI Career Accelerator
Interview Preparation Domain-specific Track
This program trains TPMs to own AI systems end to end, from coordination to production scale.
This program trains TPMs to own AI systems end to end, from orchestration to production scale.
Project: BankCo Premium Retention Agent
Outcome: Build intuition for agentic AI systems and deploy a first working agent.
Project: E-commerce customer support agent using Zapier
Outcome: Understand agent behavior and make informed design tradeoffs.
Project: NovaCart Internal Knowledge Agent using n8n
Outcome: Design reliable RAG systems that reduce hallucinations.
Project: Multi-Agent Decision Copilot using LangFlow
Outcome: Design and operate coordinated multi-agent systems.
Project: Product Launch Command Center using ElevenLabs
Outcome: Build stateful conversational systems with voice and multimodal inputs.
Project: PM Digest System Blueprint using n8n
Outcome: Understand AI system architectures and plan integration flows with delivery constraints.
Project: Evaluate & Optimize a Multi-Agent Pipeline with Langfuse
Outcome: Define KPIs, evaluate cost and safety, and build AI system dashboards for delivery readiness.
Project: 2-Agent Feedback Pipeline using n8n and OpenAI, evaluated with DeepEval
Outcome: Evaluate when personalization is justified and deploy customized model endpoints responsibly.
Project: AI-Powered Project Intelligence Assistant / AI-Powered Productivity Assistant / BYOP
Outcome: Train to own agentic AI systems end to end from orchestration to production scale.
Outcome: Frame AI initiatives correctly and define success metrics for launch readiness.
Outcome: Design and explain end-to-end agentic system architecture in technical interviews.
Outcome: Define evaluation strategies and govern AI systems with appropriate risk controls.
Outcome: Plan phased rollouts, manage incidents, and scale AI systems responsibly across teams.
*TPM interview prep is available with EdgeUp
This curriculum trains TPMs to own AI systems end to end, from orchestration and risk to production launch and scale, the way AI platforms are run in 2026.
Project: BankCo Premium Retention Agent
Outcome: Build intuition for agentic AI systems and deploy a first working agent.
Project: E-commerce customer support agent using Zapier
Outcome: Understand agent behavior and make informed design tradeoffs.
Project: NovaCart Internal Knowledge Agent using n8n
Outcome: Design reliable RAG systems that reduce hallucinations.
Project: Multi-Agent Decision Copilot using LangFlow
Outcome: Design and operate coordinated multi-agent systems.
Project: Product Launch Command Center using ElevenLabs
Outcome: Build stateful conversational systems with voice and multimodal inputs.
Project: PM Digest System Blueprint using n8n
Outcome: Understand AI system architectures and plan integration flows with delivery constraints.
Project: Evaluate & Optimize a Multi-Agent Pipeline with Langfuse
Outcome: Define KPIs, evaluate cost and safety, and build AI system dashboards for delivery readiness.
Project: 2-Agent Feedback Pipeline using n8n and OpenAI, evaluated with DeepEval
Outcome: Evaluate when personalization is justified and deploy customized model endpoints responsibly.
Project: AI-Powered Project Intelligence Assistant / AI-Powered Productivity Assistant / BYOP
Outcome: Train to own agentic AI systems end to end from orchestration to production scale.
Outcome: Frame AI initiatives correctly and define success metrics for launch readiness.
Outcome: Design and explain end-to-end agentic system architecture in technical interviews.
Outcome: Define evaluation strategies and govern AI systems with appropriate risk controls.
Outcome: Plan phased rollouts, manage incidents, and scale AI systems responsibly across teams.
*TPM interview prep is available with EdgeUp
This curriculum trains TPMs to own AI systems end to end, from orchestration and risk to production launch and scale, the way AI platforms are run in 2026.
Build an E-Commerce Customer Support Agent in Zapier Agents that reads customer reviews from Google Sheets, analyzes sentiment, drafts empathetic email responses for negative reviews, and marks positive or neutral reviews as “No response required” back in the sheet.
Build an n8n-based RAG agent that ingests NovaCart reports, product catalogs, and sales metrics from Google Drive, embeds them with OpenAI Embeddings, and stores them in Pinecone. The agent answers leadership questions using retrieved internal sources, improved by Cohere Reranker for relevance and Window Buffer Memory for conversational context, delivering citation-backed responses grounded only in company data.
Build a Multi-Agent Decision Copilot in LangFlow that transforms a product decision brief from Notion into a polished Decision Memo. Planner, Researcher, parallel Analyzers, Synthesis, Critic, Reviser, and Publisher agents collaborate to research, evaluate, red-team, and refine outputs, demonstrating no-code orchestration, review and critique loops, and tradeoffs between accuracy, latency, cost, and coordination complexity.
Build a multi-agent voice AI system in ElevenLabs where a Greeter node routes callers to Technical Readiness, GTM and Marketing, or Executive Briefing specialist subagents, each scoped to its own knowledge base. Uses forward routing, backward cross-routing, and ElevenLabs Flash speech-to-speech for low-latency, grounded, role-specific responses in live conversations.
Build an n8n-based AI workflow that listens for team questions in Slack via webhook, fetches live project data from Google Sheets, and uses an OpenAI-powered Project Status Agent to return Slack-formatted answers in-thread. The workflow handles routing, filters bot and retry events, and routes failures to a dedicated error channel, showing how agent reasoning and deterministic automation combine in production.
Build an evaluation and optimization workflow connecting a multi-agent decision pipeline in LangFlow to Langfuse for end-to-end tracing, LLM-as-a-Judge scoring, and human annotation queues. Catch a hallucinated trace on vague input, measure its impact on groundedness, tokens, and cost, then validate one prompt fix that measurably reduces hallucination, turning evaluation into a concrete optimize-then-prove loop.
Build an n8n workflow that chains two fine-tuned models to turn raw user feedback into engineering-ready tickets. A Feedback Classifier extracts technical signals and an Engineering Insight Writer converts them into structured tickets with reproduction steps and investigation checklists. Both models are fine-tuned via the OpenAI Fine-Tuning API and evaluated against the base model using DeepEval to measure the lift from fine-tuning.
Build an E-Commerce Customer Support Agent in Zapier Agents that reads customer reviews from Google Sheets, analyzes sentiment, drafts empathetic email responses for negative reviews, and marks positive or neutral reviews as “No response required” back in the sheet.
Build an n8n-based RAG agent that ingests NovaCart reports, product catalogs, and sales metrics from Google Drive, embeds them with OpenAI Embeddings, and stores them in Pinecone. The agent answers leadership questions using retrieved internal sources, improved by Cohere Reranker for relevance and Window Buffer Memory for conversational context, delivering citation-backed responses grounded only in company data.
Build a Multi-Agent Decision Copilot in LangFlow that transforms a product decision brief from Notion into a polished Decision Memo. Planner, Researcher, parallel Analyzers, Synthesis, Critic, Reviser, and Publisher agents collaborate to research, evaluate, red-team, and refine outputs, demonstrating no-code orchestration, review and critique loops, and tradeoffs between accuracy, latency, cost, and coordination complexity.
Build a multi-agent voice AI system in ElevenLabs where a Greeter node routes callers to Technical Readiness, GTM and Marketing, or Executive Briefing specialist subagents, each scoped to its own knowledge base. Uses forward routing, backward cross-routing, and ElevenLabs Flash speech-to-speech for low-latency, grounded, role-specific responses in live conversations.
Build an n8n-based AI workflow that listens for team questions in Slack via webhook, fetches live project data from Google Sheets, and uses an OpenAI-powered Project Status Agent to return Slack-formatted answers in-thread. The workflow handles routing, filters bot and retry events, and routes failures to a dedicated error channel, showing how agent reasoning and deterministic automation combine in production.
Build an evaluation and optimization workflow connecting a multi-agent decision pipeline in LangFlow to Langfuse for end-to-end tracing, LLM-as-a-Judge scoring, and human annotation queues. Catch a hallucinated trace on vague input, measure its impact on groundedness, tokens, and cost, then validate one prompt fix that measurably reduces hallucination, turning evaluation into a concrete optimize-then-prove loop.
Build an n8n workflow that chains two fine-tuned models to turn raw user feedback into engineering-ready tickets. A Feedback Classifier extracts technical signals and an Engineering Insight Writer converts them into structured tickets with reproduction steps and investigation checklists. Both models are fine-tuned via the OpenAI Fine-Tuning API and evaluated against the base model using DeepEval to measure the lift from fine-tuning.
Build a multi-agent AI assistant that automates project planning, risk assessment, and status reporting for Nexora’s TPMs. A Router orchestration pattern routes requests to specialist Planner, Risk Assessor, and Status Reporter agents grounded in real project data, reducing manual effort and flagging at-risk milestones early with human-in-the-loop review.
Build a multi-agent AI assistant that automates daily briefings, smart scheduling, and email summarization for ServionIQ’s TPMs. A Router pattern routes calendar events, email threads, and requests to specialist agents grounded in real Calendar and Email API data, cutting meeting logistics overhead and surfacing priority emails early.
Work on a project of your choice with mentorship, structured guidance, and feedback aligned to industry standards, with support on selecting the right tools and frameworks.
Choose from one of 10 Capstone Projects.
Build a multi-agent AI assistant that automates project planning, risk assessment, and status reporting for Nexora’s TPMs. A Router orchestration pattern routes requests to specialist Planner, Risk Assessor, and Status Reporter agents grounded in real project data, reducing manual effort and flagging at-risk milestones early with human-in-the-loop review.
Build a multi-agent AI assistant that automates daily briefings, smart scheduling, and email summarization for ServionIQ’s TPMs. A Router pattern routes calendar events, email threads, and requests to specialist agents grounded in real Calendar and Email API data, cutting meeting logistics overhead and surfacing priority emails early.
Work on a project of your choice with mentorship, structured guidance, and feedback aligned to industry standards, with support on selecting the right tools and frameworks.
+ 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.
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?
Technical Program Managers (TPMs):
Learn to design AI-first execution strategies
Orchestrate multi-agent workflows at scale
Bridge engineering, strategy, and delivery in AI programs
Time Zone:
Join 25,000+ tech professionals who’ve accelerated their careers with cutting-edge AI skills
25,000+ Professionals Trained
₹23 LPA Average Hike 60% Average Hike
600+ MAANG+ Instructors
Webinar Slot Blocked
Register for our webinar
Learn about hiring processes, interview strategies. Find the best course for you.
Time Zone: Asia/Kolkata
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
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
Webinar Slot Blocked
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
See you there!