Bridge Data Engineering and AI—Seamlessly to Crack Top-Tier Roles

Design, deploy, and operate production-grade agentic AI systems, owning data, retrieval, orchestration, evaluation, and ops the way Data Engineers will in 2026.

Built for Data Engineers responsible for reliability, scalability, and post-production performance of AI systems.

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Program Overview

Who It’s For

Data Engineers owning agentic AI systems in production
Engineers building data platforms, pipelines, and reliability layers
Professionals moving into AI platform and applied AI engineering roles

Program Duration

34+ weeks from agentic AI foundations to production systems
Structured for real-world data and AI ownership
Designed to run alongside a full-time role

Live Learning

70+ hours with MAANG+ Data Engineers and AI platform leaders
30+ hours of hands-on system building
Deep dives into observability, cost, and production failures

Real-World Projects

8 agentic AI live projects focused on data and retrieval systems
1 capstone or BYOP
Built to mirror real AI data workflows

Instructors

MAANG+ Data Engineers and AI platform owners
Practitioners running large-scale agentic systems
Guidance grounded in real production experience

What You’ll Learn

Agentic data pipelines, RAG systems, and orchestration
Data quality, evaluation, observability, and guardrails
Cost, latency, CI/CD, and AI system operations

Production Ops and Reliability

Monitor, debug, and operate AI systems post-launch
Handle incidents, migrations, and versioning
Own cost control and reliability at scale

Agentic AI & Domain-specific Interview Prep for DEs

Targeted preparation for AI-first Data Engineering roles
Agentic System Design and Data + AI architecture discussions
Prepare for DE-specific interviews

This program prepares Data Engineers to run AI systems in production, from data ingestion to evaluation, observability, and scale, the way DE roles will be defined in 2026 and beyond.

Average salary

1 LPA

Professionals trained

0 +

Avg. ROI on course price

5x- 5 x

30+ Tools & Tech You’ll Learn

Why DEs Choose This Agentic AI & Interview Prep Program

Built for Real Production Data Systems

Design and operate agentic AI pipelines that run in production
Reflect how retrieval, orchestration, and evaluation work in real data platforms
Move beyond experiments to reliable, monitored AI systems

What Data Engineers Actually Own

Agentic data pipelines, RAG reliability, and schema-aware orchestration
Data quality, evaluation workflows, and observability
Cost, latency, and SLA-driven production constraints

Learn from Practitioners Running It Today

Guided by 700+ MAANG+ Data Engineers and AI platform builders
Learn directly from teams operating large-scale agentic systems
Practical insights grounded in real production ownership

Build Portfolio-Ready Production Systems

Build retrieval pipelines and evaluation workflows
Implement data quality agents and monitoring systems
Ship production-ready AI data pipelines

Structured for Working Data Engineers

Systems-first learning that fits real DE workflows
Designed to build production readiness, not tool familiarity
Progress from foundations to operations without overload

Results Data Engineers Trust

Trusted by thousands of professionals globally
NPS of 55 with a 4.75+ learner rating
Outcomes driven by applied, production-grade learning

This program prepares Data Engineers to own AI systems in production, from data ingestion and retrieval to evaluation, observability, and cost control, the way DE roles will be defined in 2026 and beyond.

Assess Your Fit

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Detailed Curriculum: Agentic AI for Data Engineers

2026-Ready Applied Agentic AI for Data Engineers

Applied Agentic AI Core for DEs — Week-by-Week Curriculum

Week 0: Pre-Program Foundations

Colab notebook setup and OpenAI API key management
Data handling and inspection using Pandas
Designing strict system prompts for data-grounded agents
End-to-end agent flow: input, reasoning, data access, and response

Project: CSV FAQ Agent

Outcome: Set up the stack and deploy a working agent.

Week 1: Agentic AI Foundations & Reflex Agents

Evolution of AI from rule-based systems to GenAI and LLMs
The agent equation: Agent = (Prompt + Tools + Memory) x LLM
The ReAct loop: Thought, Action, Observation, and Repeat
Agentic design patterns: Reflection, Routing, Tool Use, Planning, Multi-Agent

Project: CRM Lead Qualifier Agent

Outcome: Design predictable and controllable agents.

Week 2: RAG-Powered Knowledge Agents — I

Embeddings, chunking strategies, and vector stores
FAISS and Chroma fundamentals
Offline indexing pipeline: document ingestion, chunking, embedding, and storage
Indexing strategies: vector, summary, tree, keyword, and hybrid

Project: SupportDesk-RAG: Retrieval Layer

Outcome: Build reliable RAG systems backed by data quality.

Week 3: RAG-Powered Knowledge Agents — II

RAG pipeline architecture: retrieve, augment, and generate
Multi-turn RAG with conversation history
Retrieval metrics: Precision at K, Recall at K, and F1 at K
Agentic RAG architectures and tool orchestration

Project: SupportDesk-RAG: End-to-End Assistant

Outcome: Build and evaluate a full RAG pipeline that reduces hallucinations.

Week 4: Multi-Agent Systems

Limitations of single-agent systems for complex tasks
Role-based agent design: orchestrator, search, planner, and synthesizer patterns
State, nodes, and edges as core building blocks in LangGraph
Parallelization and subagent design for specialized domains

Project: Multi-Agent Travel Planner

Outcome: Design coordinated multi-agent workflows.

Week 5: Conversational & Multimodal Agents

Voice agent architectures: cascaded pipeline and speech-to-speech realtime patterns
Subgraphs in LangGraph for node reuse and multi-agent coordination
Human-in-the-loop patterns using interrupts to pause, review, approve, and resume
Parallel agent routing and synthesizer design for unified output

Project: E-Commerce AI Assistant

Outcome: Build stateful conversational agents with voice and multimodal support.

Week 6: Agent Communication Protocols (MCP, A2A, ACP)

Finite State Machines with explicit terminal states and validated transition maps
Structured tool access using Model Context Protocol and FastMCP servers
Graph-based orchestration using LangGraph with nodes, conditional edges, and shared state
Networked agent deployment using the A2A protocol with Agent Cards and JSON-RPC over HTTP

Project: Real Estate Negotiation Simulator

Outcome: Design structured and debuggable agent communication.

Week 7: Hybrid Search & Retrieval

Sparse and dense vectors, distance metrics, and nearest neighbor search
Inverted index, lexical search, and SPLADE
Hybrid search: indexing and querying with Reciprocal Rank Fusion
Tools and libraries: Qdrant, FastEmbed, Hugging Face, BGE

Project: Hybrid Product Search Agent

Outcome: Build reliable hybrid retrieval pipelines combining semantic and lexical search.

Week 8: Agent Observability, Evaluation and Safety

Structured agent observability using LangSmith traces, runs, and hierarchical span trees
LLM-as-judge and G-Eval evaluators for correctness and helpfulness scoring
Progressive output guardrails using Guardrails AI with regex, Pydantic schema, and semantic validators
Cost optimization: model routing, semantic caching, and the OpenAI Batch API

Project: Production-Ready Fintech Support Agent

Outcome: Operate agents safely, with full observability and cost control.

Week 9: Fine-Tuning & Domain Adaptation

Decision framework: prompt engineering vs RAG vs fine-tuning
Fine-tuning landscape: LoRA, QLoRA, prefix tuning, adapter layers, and IA3
Model loading with 4-bit quantization, LoRA adapter training, and Hugging Face Hub deployment
Evaluation using LLM-as-judge logged to LangSmith

Project: Domain-Specific Fine-Tuned Agent

Outcome: Decide when fine-tuning is justified and integrate responsibly.

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

End-to-end multi-agent architecture with retrieval, orchestration, evaluation, and safety
System design covering reliability, latency, cost, and production constraints
Deployment using LangGraph, LangChain, Streamlit, and AWS
Failure testing, iteration, and architectural justification

Project: Capstone (curated tracks) or BYOP

Outcome: Design and ship a production-grade agentic AI system.

Agentic AI System Design and Interview Preparation for DEs

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 with citations
Memory design: short-term context and long-term cached research results

Outcome: Design reliable research agents with structured planning and tool guardrails.

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

Agent workflow: schema retrieval, query generation, execution, and refinement
Text-to-SQL design: table selection, query generation, and query verification
Guardrails: preventing dangerous queries, limiting database access, and query validation
Evaluation: query correctness, semantic validation, and execution-based evaluation

Outcome: Build and defend reliable Text-to-SQL agents that handle real database complexity.

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

Planner, researcher, executor agents with centralized vs decentralized coordination
Shared memory design: task state, intermediate outputs, and historical decisions
Reliability: agent disagreement, cascading failures, and preventing infinite loops
Observability: tracing across agents and per-agent success rate and latency metrics

Outcome: Design multi-agent systems that coordinate reliably and terminate correctly.

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

Verification-based agent workflow: generate, test, and refine
Evaluation systems: test execution as ground truth, static analysis, and ranking candidate fixes
Observability: tracking attempts per fix, success rate, and failure reasons
Productionization: sandboxed execution, compute limits, and rollback mechanisms

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

Data Engineering Interview Prep

Algorithms

System Design

Career Session, Orientation & Offer Strategy

SQL Programming

Data Modeling

ETL & Pipeline Design

Data Platforms

This curriculum prepares Data Engineers to own AI systems in production, from data ingestion and retrieval to evaluation, observability, guardrails, and cost-efficient operations, the DE mandate in 2026 and beyond.

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Live Guided Projects

P1CRM Lead Qualifier Agent

Build your first LLM-powered agent to automate sales workflows using function calling, domain lookup, CRM history check, and lead scoring via a think-act-observe loop.

P2SupportDesk-RAG

Build a production-ready RAG system for IT support troubleshooting using LangChain, LlamaIndex, FAISS, and Chroma with five indexing approaches, anti-hallucination safeguards, and two-layer retrieval and generation evaluation

P3Multi-Agent Travel Planner

Design a multi-agent travel planning workflow using an Orchestrator, Search, Itinerary Planner, and Synthesizer pattern built with LangGraph, LangChain, Tavily API, and SerpAPI covering routing, parallelization, and state management.

P4AxiomCart - Multi-Agent Voice-Enabled Shopping Assistant

Build a stateful voice-enabled shopping assistant with a LangGraph StateGraph, RAG-powered product discovery, Human-in-the-Loop order tracking, parallel agent dispatch, and a Whisper and OpenAI TTS voice pipeline.

P5Real Estate Negotiation Simulator

Build a buyer-seller negotiation system using typed Pydantic message schemas, FSM terminal states, MCP-grounded pricing tools, LangGraph routing, and A2A protocol transport via Google ADK with failure safeguards.

P6Hybrid Product Search Agent

Build a hybrid product search system over Amazon’s ESCI dataset combining SPLADE sparse and BGE-Large dense embeddings indexed in Qdrant, merged via Reciprocal Rank Fusion for precise context-aware product retrieval.

P7Production-Ready Fintech Support Agent

Build a multi-agent fintech support system with LangSmith tracing, DeepEval metrics, Guardrails AI validators, Microsoft Presidio PII redaction, and tiktoken-powered cost-per-query dashboards.

P8Domain-Specific Fine-Tuned Agent

Fine-tune a 4-bit quantized Qwen2.5-1.5B-Instruct with QLoRA for healthcare Q&A, deploy the LoRA adapter to Hugging Face Hub, and evaluate against the base model using LLM-as-judge across accuracy, helpfulness, and safety.

Live Guided Projects

P1CRM Lead Qualifier Agent

Build your first LLM-powered agent to automate sales workflows using function calling, domain lookup, CRM history check, and lead scoring via a think-act-observe loop.

P2SupportDesk-RAG

P3Multi-Agent Travel Planner

P4AxiomCart - Multi-Agent Voice-Enabled Shopping Assistant

P5Real Estate Negotiation Simulator

P6Hybrid Product Search Agent

P7Production-Ready Fintech Support Agent

Build a multi-agent fintech support system with LangSmith tracing, DeepEval metrics, Guardrails AI validators, Microsoft Presidio PII redaction, and tiktoken-powered cost-per-query dashboards.

P8Domain-Specific Fine-Tuned Agent

Capstone Projects

Capstones stay aligned with industry needs. Pick from 4 production-grade projects to build your portfolio.

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.

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.

Capstone Projects

Capstones stay aligned with industry needs. Pick from 4 production-grade projects to build your portfolio.

Autonomous ETL/ELT Agent for DevOps-Driven Data Engineering

Intelligent Data Quality System

Industry-Wide Financial Trend Analysis

BYOP (Bring Your Own Project)

+ 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

Harshit Laddha

Senior Software Engineer

Prateek Singhal

Data Engineer

Anshaj Khare

Software Engineer

Ishaan Sutaria

Head of Engineering

Shashank Gupta

Senior Applied Scientist

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?

Other Tech Professionals (Cloud, DevOps, Security, Data, ML):

Learn agent-based automation for cloud, security & infra ops

Integrate LLMOps, RAG, and orchestration into production stacks

Build reliable, scalable AI pipelines across domains