Applied Agentic AI for Data Engineers Building Production Systems

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

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

Real-World Projects

3 live, expert-guided projects focused on data and retrieval systems
4 production-grade capstone options
Built to mirror real AI data workflows

Instructors

FAANG+ 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 Interview Preparation

Interview prep for AI-first Data Engineering roles
Agentic System design and data + AI architecture discussions
Production reliability and tradeoff scenarios

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.

Careers transformed

Average package for alumni

$ 0

Average ROI on course price

0 x

30+ Tools & Tech You’ll Learn

Why DEs Choose This Applied Agentic AI 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+ FAANG+ 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.

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Detailed Curriculum: Applied Agentic AI for DEs

Applied Agentic AI Core for DEs

Week 0: Foundations and Environment Setup

Environment setup, APIs, JSON schemas, and minimal agents
Agent lifecycle basics and execution flow
First working agent deployment

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

Week 1: Agentic Foundations and Reflex Agents

Reflex and tool-based agents
Modular design using rules and LLM reasoning
Control flow and determinism tradeoffs

Outcome: Design predictable and controllable agents.

Week 2: RAG-Powered Knowledge Agents

Embeddings, chunking, and retrieval pipelines
Grounded response generation
Retrieval evaluation to reduce hallucinations

Outcome: Build reliable RAG systems backed by data quality.

Week 3: Multi-Agent Orchestration

Planner–Executor–Critic patterns
Role-based task decomposition
Cost and latency tradeoffs

Outcome: Design coordinated multi-agent workflows.

Week 4: Conversational and Multimodal Agents

Memory strategies for long-running agents
Voice and multimodal interfaces
Persona and conversation control

Outcome: Build stateful conversational agents.

Week 5: Agent Communication Protocols

MCP, A2A, ACP, and async workflows
Structured messaging and agent graphs
Replay-based debugging

Outcome: Design structured and debuggable agent communication.

Week 6: Domain-Specific and Vertical Agents

API integrations and domain logic
JSON-schema outputs and validation
Fault tolerance patterns

Outcome: Build domain-ready agent pipelines.

Week 7: Summarization and Recommendation Systems

Ranking and summarization pipelines
Decision support agents
Production data dashboards

Outcome: Ship agents that support real decisions.

Week 8: Safety, Evaluation, and Cost Control

Guardrails, red-teaming, and fallback design
Evaluation strategies
Cost and latency optimization

Outcome: Operate agents safely and efficiently.

Week 9: Fine-Tuning and Model Integration

LoRA and domain adaptation concepts
Model routing and serving
Accuracy vs cost tradeoffs

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

Week 10: Enterprise Capstone System

End-to-end multi-agent architecture
Deployment, CI/CD, and scaling
Production architecture review

Outcome: Design a production-grade agentic AI data platform.

Agentic AI System Design and Interview Preparation for DEs

Weeks 11–12: Agentic System Design Patterns

When to use agents vs simpler architectures
Single-agent vs multi-agent decisions
Structured outputs and tool use

Outcome: Explain design choices clearly in interviews.

Weeks 13–14: Orchestration, Memory, and Data

Runtime orchestration and retries
Memory strategies and debugging
Structured and unstructured data handling

Outcome: Defend orchestration and data decisions with confidence.

Weeks 15–16: Evaluation, Observability, and Guardrails

Component and system-level evaluation
Observability signals and debugging
Prompt misuse and access control

Outcome: Handle safety and reliability questions in senior DE interviews.

Week 17: Cost and Production Operations

Cost and latency optimization
Deployment patterns and versioning
Incidents, migrations, and rollbacks

Outcome: Demonstrate production ownership in AI system interviews.

AI DE-Specific Career Guidance & 1:1 Mentoring

Foundational Materials

Python Fundamentals Refresher

Evolution of GenAI

ML Foundations

Specialized Sessions

Laying the Groundwork for AI-Driven Development

Hands-on with Generative AI Models

Building Effective Prompts and Configuration-Driven Apps

Innovating with Multi-Agent Systems and Specialized Models

Harnessing LLM Frameworks for Real-World Development

From Development to Deployment: Scaling and Debugging AI Models

Detailed Curriculum: Applied Agentic AI for DEs

Applied Agentic AI Core for DEs

Week 0: Foundations and Environment Setup

Environment setup, APIs, JSON schemas, and minimal agents
Agent lifecycle basics and execution flow
First working agent deployment

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

Week 1: Agentic Foundations and Reflex Agents

Reflex and tool-based agents
Modular design using rules and LLM reasoning
Control flow and determinism tradeoffs

Outcome: Design predictable and controllable agents.

Week 2: RAG-Powered Knowledge Agents

Embeddings, chunking, and retrieval pipelines
Grounded response generation
Retrieval evaluation to reduce hallucinations

Outcome: Build reliable RAG systems backed by data quality.

Week 3: Multi-Agent Orchestration

Planner–Executor–Critic patterns
Role-based task decomposition
Cost and latency tradeoffs

Outcome: Design coordinated multi-agent workflows.

Week 4: Conversational and Multimodal Agents

Memory strategies for long-running agents
Voice and multimodal interfaces
Persona and conversation control

Outcome: Build stateful conversational agents.

Week 5: Agent Communication Protocols

MCP, A2A, ACP, and async workflows
Structured messaging and agent graphs
Replay-based debugging

Outcome: Design structured and debuggable agent communication.

Week 6: Domain-Specific and Vertical Agents

API integrations and domain logic
JSON-schema outputs and validation
Fault tolerance patterns

Outcome: Build domain-ready agent pipelines.

Week 7: Summarization and Recommendation Systems

Ranking and summarization pipelines
Decision support agents
Production data dashboards

Outcome: Ship agents that support real decisions.

Week 8: Safety, Evaluation, and Cost Control

Guardrails, red-teaming, and fallback design
Evaluation strategies
Cost and latency optimization

Outcome: Operate agents safely and efficiently.

Week 9: Fine-Tuning and Model Integration

LoRA and domain adaptation concepts
Model routing and serving
Accuracy vs cost tradeoffs

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

Week 10: Enterprise Capstone System

End-to-end multi-agent architecture
Deployment, CI/CD, and scaling
Production architecture review

Outcome: Design a production-grade agentic AI data platform.

Agentic AI System Design and Interview Preparation for DEs

Weeks 11–12: Agentic System Design Patterns

When to use agents vs simpler architectures
Single-agent vs multi-agent decisions
Structured outputs and tool use

Outcome: Explain design choices clearly in interviews.

Weeks 13–14: Orchestration, Memory, and Data

Runtime orchestration and retries
Memory strategies and debugging
Structured and unstructured data handling

Outcome: Defend orchestration and data decisions with confidence.

Weeks 15–16: Evaluation, Observability, and Guardrails

Component and system-level evaluation
Observability signals and debugging
Prompt misuse and access control

Outcome: Handle safety and reliability questions in senior DE interviews.

Week 17: Cost and Production Operations

Cost and latency optimization
Deployment patterns and versioning
Incidents, migrations, and rollbacks

Outcome: Demonstrate production ownership in AI system interviews.

Live Guided Projects

First LLM-powered Agent

Build your first LLM-powered agent to understand how agents differ from chatbots. Learn how LLMs act as reasoning engines, how tools and memory fit into agent architecture, and how agent behavior is controlled.

Knowledge Assistant (RAG with Evaluation)

Build a document-grounded knowledge assistant using Retrieval-Augmented Generation (RAG). Ingest PDFs and text files, design chunking strategies, retrieve relevant context, and evaluate retrieval quality to prevent hallucinations.

Multi-Agent Research Team

Design a multi-agent system using a Planner → Executor → Critic pattern. Agents collaborate to research, summarize, and critique outputs, demonstrating task decomposition, coordination, and quality loops.

Conversational Research Assistant

Build a stateful, voice-enabled conversational agent with memory. Handle multi-turn conversations, track intent across sessions, and explore memory drift and summarization trade-offs in long-running agents.

Negotiation Simulator

Create a buyer–seller negotiation system where agents communicate using structured protocol messages instead of free text. Implement state transitions, branching logic, retries, and error handling to prevent loops and ambiguity.

Price Comparison Agent

Build a domain-specific vertical agent that compares data from multiple APIs and sources. Handle authentication, rate limits, retries, caching, and return structured, schema-validated insights.

Decision Support Agent

Design a recommendation pipeline that summarizes information, scores options, ranks results, and presents insights through a visual dashboard. Emphasize faithfulness, bias awareness, and human-in-the-loop review.

Production-Ready Support Agent

Build a production-ready customer support agent with RAG, safety guardrails, evaluation pipelines, and cost/latency dashboards. Learn how to operate agents responsibly under real-world constraints.

Domain-Specific Fine-Tuned Agent

Build a domain-adapted agent by fine-tuning a language model for a specific vertical such as Finance, Healthcare, or SaaS. Learn how to prepare datasets, apply parameter-efficient fine-tuning techniques, and integrate the fine-tuned model into an existing agent workflow. Evaluate performance improvements against prompting and RAG baselines, and analyze cost–benefit trade-offs to decide when fine-tuning is justified in real-world systems

Live Guided Projects and Capstone Projects

First LLM-powered Agent

Knowledge Assistant (RAG with Evaluation)

Multi-Agent Research Team

Conversational Research Assistant

Negotiation Simulator

Price Comparison Agent

Build a domain-specific vertical agent that compares data from multiple APIs and sources. Handle authentication, rate limits, retries, caching, and return structured, schema-validated insights.

Decision Support Agent

Production-Ready Support Agent

Build a production-ready customer support agent with RAG, safety guardrails, evaluation pipelines, and cost/latency dashboards. Learn how to operate agents responsibly under real-world constraints.

Domain-Specific Fine-Tuned Agent

Projects are subject to change as per industry inputs.

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.

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.

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

Automated Data Insights Generator

FAANG+ Instructors to Train You

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

Vijay Swamy

Senior Data Engineer

Rishabh Misra

Lead ML Engineer & Researcher

Mickael Savafi

Staff Software Engineer

Ram Vegiraju

ML Architect @ AWS

Randy Cogill

Senior Research Scientist

Samwel Emmanuel

Data & AI Solutions Architect @ Databricks

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Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.

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Senior Director - ISV/SolEx Technical Partner Management

I highly recommend the Applied GenAI course by Interview Kickstart. The PM path was incredibly well-organized, reshaping my thinking on how to leverage Generative AI in product management. The hands-on approach, insightful curriculum, and experienced instructors made it an outstanding learning experience!

Amit Vaswani

Lead Software Engineer, Highnote

I recently completed the Applied Gen AI course at Interview Kickstart, and I couldn't be more impressed. The course was well-structured into modules, making complex concepts easier to digest. For someone without a strong programming background, I appreciated the beginner Python class they offer for non-programmers—it helped build a solid foundation. The instructors are fantastic and always go the extra mile to ensure every question is answered. They are patient and don’t rush through the material, often allowing classes to run over to ensure everyone fully grasps the concepts. One of my favorite features is the 'Expert Connect,' where you can have 1-on-1 sessions with instructors to clear any doubts. Overall, Interview Kickstart provided an exceptional learning experience, and I highly recommend it to anyone looking to uplevel in their career.

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FAQs

What is Applied Agentic AI for Data Engineers?

This is a comprehensive 14-week blended learning program designed specifically for data engineers to master AI-driven development skills. The course covers everything from foundational AI concepts to building and deploying autonomous multi-agent systems, with a focus on practical, real-world applications in data engineering.

Do I need prior AI or Machine Learning experience?

No prior AI or ML knowledge is required. The course starts with Python fundamentals and gradually progresses through GenAI basics, LLM frameworks, and advanced multi-agent systems. All necessary concepts are covered from the ground up.

What programming skills do I need?

You should have:

Strong understanding of data engineering fundamentals
Proficiency in Python or similar programming languages
Experience with data pipeline tools and frameworks
Familiarity with SQL and database concepts

If you need a Python refresher, the course includes foundational Python modules specifically for GenAI applications.

How is the Applied Agentic AI for Data Engineers Course delivered?

The program uses a blended learning approach:

Live Interactive Sessions: Every Sunday with FAANG+ instructors
Self-Paced Modules: Pre-recorded specialized sessions for deep dives
Hands-On Projects: Live guided projects with instructor support
Capstone Projects: Domain-specific projects with weekly feedback
Expert Connect: 1-on-1 sessions with instructors for personalized guidance

What is the weekly time commitment?

Participants should expect to dedicate:

4–6 hours per week for live sessions and interactive learning
6–8 hours per week for assignments, projects, and self-paced modules
Total: 10–14 hours per week for optimal learning outcomes

What is the duration of the Applied Agentic AI for Data Engineers course?

The program runs for 14 weeks, including:

Weeks 0-10: Core curriculum and live guided projects
Weeks 11-14: Data Engineering capstone projects
Floater sessions and self-paced learning materials

What topics are covered in the curriculum?

The comprehensive curriculum includes:

Foundations: Python for GenAI, ML fundamentals, AI-powered development
Agentic AI Core: RAG pipelines, vector databases, prompt engineering, AI agents
Multi-Agent Systems: LangChain, LangGraph, CrewAI, agent orchestration
Advanced Topics: LLM architecture, fine-tuning, RLHF, Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A)
Data Engineering Focus: AI in data pipelines, ETL automation, data quality intelligence, and Airflow-based orchestration
Deployment: LLMOps, Docker, Kubernetes, monitoring, and production best practices

What are Live Guided Projects?

Live Guided Projects are instructor-led, hands-on sessions where you build complete AI applications from scratch with real-time guidance. These are code-along sessions designed to give you confidence in building AI systems without being overwhelmed. They’re part of the core curriculum and don’t require independent submissions.

What are Capstone Projects and how are they different?

Capstone Projects are learner-driven, allowing you to independently design and implement AI solutions. Unlike Live Guided Projects:

You lead the implementation with instructor guidance
Introduced at the beginning of the DE Pathway (Week 11)
You work on them over 4 weeks with continual feedback
Include interim connects and Q&A sessions
Conclude with final presentations to the entire cohort
Involve peer grading and feedback

Can I choose my own Capstone Project?

Yes! The course offers a BYOP (Bring Your Own Project) option. You can propose a project idea that aligns with your interests and career goals, subject to instructor approval. This allows you to build something directly relevant to your work or portfolio needs.

Will I build a portfolio during the Applied Agentic AI for Data Engineers course?

Absolutely. By the end of the program, you’ll have:

3 complete live guided projects
1 comprehensive capstone project
Multiple assignments and mini-projects from specialized sessions
A GitHub portfolio demonstrating your AI engineering capabilities
Real-world applications that showcase your skills to potential employers

Who are the instructors of the Applied Agentic AI for Data Engineers course?

All instructors are current or former engineers from FAANG+ companies (Amazon, Google, Microsoft, Walmart) with:

5-10 years of industry experience
Hands-on expertise in building and deploying AI systems
Real-world experience in software and data engineering
Practical insights from leading Generative AI initiatives at top tech companies

What kind of instructor support is available?

You’ll receive comprehensive support through:

Live Interactive Sessions: Real-time learning with Q&A during Sunday classes
Expert Connect: 1-on-1 sessions with instructors to clarify doubts
Weekly Office Hours: Dedicated Q&A sessions for project guidance
Interim Connects: Regular feedback during capstone project development
Peer Collaboration: Learning from cohort members during presentations
Operations Team Support: Technical assistance with platform access and content

What career outcomes can I expect?

After completing this course, you’ll be positioned for roles such as:

AI-Powered Data Engineer
Data Engineer with AI Expertise
ML Engineer (Data Focus)
AI Solutions Architect
Data Engineer in AI-Powered Workflows
AI Consultant specializing in data systems

How will this course future-proof my career?

The course prepares you for the AI-first future by:

Teaching autonomous multi-agent systems that are transforming data engineering
Providing expertise in high-demand skills like RAG, LLM orchestration, and agent frameworks
Building practical experience with production-ready AI deployments
Covering emerging protocols like MCP and A2A that define next-gen AI systems
Positioning you to lead AI-driven data projects in your organization

Will I receive a certificate?

Yes, participants who successfully complete the program, including capstone projects, will receive a Certificate of Completion along with personalized feedback from instructors.

How does this course compare to other AI programs?

This course stands out through:

Data Engineering Focus: Tailored specifically for DE professionals, not generic AI training
FAANG+ Mentorship: Learn from 700+ experts at top tech companies
Integrated Capstone: 4-week guided capstone projects with weekly feedback
Live Guided Projects: Instructor-led builds, not just theory
Specialized Sessions: Deep dives into MCP, A2A, ADK, and production deployment

When do cohorts start?

New cohorts launch once every alternate week with orientation sessions. Join our webinar for the next available start date.

What happens if I miss a live session?

All live sessions are recorded and available for review. You can catch up through recordings and self-paced materials. Office hours and Expert Connect sessions provide additional support, and the operations team can help with any content access issues.

Is there a refund policy?

Contact the operations team for detailed information about the refund policy and enrollment terms.

Can I access course materials after completion?

Yes, learners maintain access to course materials, enabling you to reference content as you apply skills in your career.

Is there a payment plan?

Yes. We offer multiple financing options to make the course more accessible to working professionals.

How do I enroll in the Applied Agentic AI for Data Engineers course?

Start with our free market webinar, and our program advisors will help you from there. Click here to register for the free session.