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 PMs to own AI product decisions end to end, the way AI products are built in 2026.
Outcome: Build strong intuition for agentic AI products and ship a first working agent using no-code tools.
Outcome: Understand how agent behavior impacts UX, cost, and product reliability.
Outcome: Design reliable RAG-based products that reduce hallucinations and improve trust.
Outcome: Define multi-agent workflows that support complex product use cases.
Outcome: Design stateful conversational AI experiences with voice and multimodal inputs.
Outcome: Make confident architecture choices for AI features and platforms.
Outcome: Define KPIs, measure output quality, and reduce hallucination and cost in AI pipelines.
Outcome: Decide when personalization and fine-tuning are justified from a product ROI lens.
Outcome: Deliver a complete agentic AI product proposal ready for stakeholder and leadership review.
Outcome: Confidently frame AI product problems and justify agentic decisions in PM interviews.
Outcome: Clearly explain AI system design and tradeoffs expected in senior PM interviews.
Outcome: Assess AI product quality, safety, and ROI with product-level clarity in interviews.
Outcome: Confidently discuss AI product launch, scale, and production operations in senior PM interviews.
Outcome: Build strong intuition for agentic AI products and ship a first working agent using no-code tools.
Outcome: Understand how agent behavior impacts UX, cost, and product reliability.
Outcome: Design reliable RAG-based products that reduce hallucinations and improve trust.
Outcome: Define multi-agent workflows that support complex product use cases.
Outcome: Design stateful conversational AI experiences with voice and multimodal inputs.
Outcome: Make confident architecture choices for AI features and platforms.
Outcome: Define KPIs, measure output quality, and reduce hallucination and cost in AI pipelines.
Outcome: Decide when personalization and fine-tuning are justified from a product ROI lens.
Outcome: Deliver a complete agentic AI product proposal ready for stakeholder and leadership review.
Outcome: Confidently frame AI product problems and justify agentic decisions in PM interviews.
Outcome: Clearly explain AI system design and tradeoffs expected in senior PM interviews.
Outcome: Assess AI product quality, safety, and ROI with product-level clarity in interviews.
Outcome: Confidently discuss AI product launch, scale, and production operations in senior PM interviews.
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 NovaCart 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, Notion-ready Decision Memo. Planner, Researcher, parallel Analyzers, Synthesis, Critic, Reviser, Confidence, 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 Product Launch Command Center — 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 LLM-condition forward routing, backward cross-routing between specialists, 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 a Google Sheets tracker, and uses an OpenAI-powered Project Status Agent to return Slack-formatted answers in-thread. The workflow handles Slack URL verification, filters out bot and retry events, and routes failures to a dedicated error channel — showing how agent reasoning and deterministic automation combine in a production-style architecture.
Build an evaluation and optimization workflow that connects a multi-agent decision pipeline in LangFlow to Langfuse for end-to-end tracing, LLM-as-a-Judge scoring, and human annotation queues. Learners 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 agent extracts technical signals and an Engineering Insight Writer agent converts that classification into a structured ticket with reproduction steps and an investigation checklist. Both models are fine-tuned on custom JSONL datasets 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 NovaCart 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, Notion-ready Decision Memo. Planner, Researcher, parallel Analyzers, Synthesis, Critic, Reviser, Confidence, 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 Product Launch Command Center — 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 LLM-condition forward routing, backward cross-routing between specialists, 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 a Google Sheets tracker, and uses an OpenAI-powered Project Status Agent to return Slack-formatted answers in-thread. The workflow handles Slack URL verification, filters out bot and retry events, and routes failures to a dedicated error channel — showing how agent reasoning and deterministic automation combine in a production-style architecture.
Build an evaluation and optimization workflow that connects a multi-agent decision pipeline in LangFlow to Langfuse for end-to-end tracing, LLM-as-a-Judge scoring, and human annotation queues. Learners 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 agent extracts technical signals and an Engineering Insight Writer agent converts that classification into a structured ticket with reproduction steps and an investigation checklist. Both models are fine-tuned on custom JSONL datasets via the OpenAI Fine-Tuning API and evaluated against the base model using DeepEval to measure the lift from fine-tuning.
Build an an AI-powered sales assistant that automates lead qualification, recommends furniture using real-time inventory, and delivers weekly sales insights for Oak & Ember Interiors. Using agentic workflows and grounded LLMs, it replaces manual processes with an accurate, scalable system that completes with human review, privacy guardrails, and measurable efficiency gains.
Build PRD Genie—an agentic AI assistant that turns meeting transcripts and notes into structured PRDs, epics, and user stories. Grounded in real inputs and designed with human review, it cuts documentation time and accelerates product delivery at NeuronForge.
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 you to build impactful solutions. You will receive guidance on selecting the right tools and frameworks based on project requirements.
Build an an AI-powered sales assistant that automates lead qualification, recommends furniture using real-time inventory, and delivers weekly sales insights for Oak & Ember Interiors. Using agentic workflows and grounded LLMs, it replaces manual processes with an accurate, scalable system that completes with human review, privacy guardrails, and measurable efficiency gains.
Build PRD Genie—an agentic AI assistant that turns meeting transcripts and notes into structured PRDs, epics, and user stories. Grounded in real inputs and designed with human review, it cuts documentation time and accelerates product delivery at NeuronForge.
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 you to build impactful solutions. You will receive guidance on selecting the right tools and frameworks based on project requirements.
+ 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?
Product Managers:
Integrate Agentic AI into product roadmaps and prototyping
Drive innovation through automation-first strategies
Gain hands-on experience with multi-agent frameworks
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Time Zone: Asia/Kolkata
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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!