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Cohort 014 · AI Engineer & AI Agents · Enrolling Now

AI Engineer
+ AI Agents

Master end-to-end AI Engineer and AI Agents with real-world, job-ready implementation skills. Build foundations in Python and SQL, ship pipelines with PySpark and Databricks, scale on Microsoft Fabric, and integrate Generative + Agentic AI into production data workflows.

3mo
duration
30+
modules
4.7/5
cohort rating
100k+
enrolled
Where our AI Engineer alumni work
MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with

Four things every AI Engineer grad walks away with.

Most programs stop at tools. Ours makes you ship pipelines, platforms, and AI-powered data products that hiring teams can verify.

01
Agent-Ready skills
Build, deploy, and monitor AI agents that run production workflows — not chatbot toys.
02
A shipped capstone
A live React + FastAPI + LangGraph app on Kubernetes, monitored, observable, public URL.
03
Verifiable credential
2026 Agent-Ready rubric, graded 1–5 with a public verification URL recruiters can check.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, resume rebuild, and warm intros to our 1,000+ hiring partners.
3 months, four phases

From "loads CSVs" to ships AI-native data pipelines.

Weeks 1–3 build Python and SQL depth. Weeks 4–7 cover Power BI and data storytelling. Weeks 8–10 move into PySpark, Databricks, and Fabric. Weeks 11–12 ship Generative + Agentic AI data agents.

WEEKS 1–3 · FOUNDATIONS

Python + SQL for AI Engineer

  • Python data structures, iterators, OOP
  • PostgreSQL querying, joins, windows, CTEs
  • Database design, indexing, optimization
  • Data file formats and transformation patterns
YOU SHIPA Python + SQL ingestion and transformation workflow over production-like datasets.
WEEKS 4–7 · ANALYTICS

Power BI and business intelligence

  • Power Query and source integrations
  • Star schema modeling and DAX measures
  • Advanced visuals, storytelling, KPI dashboards
  • Publishing, sharing, governance, refresh
YOU SHIPA complete Power BI reporting suite consumed by business teams and leaders.
WEEKS 8–10 · DATA PLATFORM

PySpark, Databricks, and Microsoft Fabric

  • Spark DataFrames, joins, windows, optimization
  • Databricks workflows, Delta Lake, Unity Catalog
  • Fabric OneLake, Lakehouse, Warehouse, RTI
  • Streaming, orchestration, and governance
YOU SHIPAn enterprise-grade ELT platform with scheduled jobs, observability, and governed data products.
WEEKS 11–12 · GENERATIVE + AGENTIC AI

Deploy AI agents that automate analytics, retrieval, and reporting across your data platform.

Use LLM APIs, LangChain, RAG, and LangGraph workflows with persistence and HITL. Add MCP tool access and enterprise guardrails. Your capstone connects pipelines, dashboards, and AI agents into a single production-ready data intelligence system.

Partner orgs (2026)48
Capstones deployed280+
→ Placement offers82%
Course curriculum

Seven sections. 65+ modules. The AI-native AI Engineer stack.

Jump to any section on the left. Click a module to see topics, hands-on lab, and key technologies.

01

Python for AI & Data

How modern apps work, how teams ship them with Agile, where compute & cloud fit, and how AI plugs into the 2026 stack.
10 MODULES
WEEK 1
Introduction to Python & Environment Setup Python Interpreter Installation (Windows/Mac) IDE Setup (Visual Studio Code) Python Syntax & Keywords (35 keywords) Identifiers & Naming Conventions Variables & Memory Management Data Types (Simple & Complex) Type Conversion & Type Casting Operators (Arithmetic, Comparison, Logical, etc.) Conditional Statements (if, elif, else, match-case) Loops (while, for) & range() function Control Flow (break, continue, pass) User Input with input() function
String Definition & Rules String Indexing (Positive & Negative) String Slicing (start:end:step) String Operations (Concatenation, Repetition) String Formatting (f-strings, format()) String Immutability Concept Case Conversion Methods Search Methods (find, index, count) Checking Methods (isalpha, isdigit, etc.) Trimming Methods (strip, lstrip, rstrip) Replacement & Split/Join Methods String Alignment Methods
Simple vs Complex Data Types Lists: Creation, Indexing, Slicing List Operations & Methods Adding Elements (append, insert, extend) Removing Elements (remove, pop, clear) Searching & Counting (index, count) Sorting & Reversing (sort, reverse) List Comprehensions Tuples: Creation & Operations Tuple Immutability Tuple Packing & Unpacking Lists vs Tuples Comparison
Key Topics : Dictionaries: Creation & Access Dictionary Operations & Methods Keys, Values, Items Methods Dictionary Comprehensions Nested Dictionaries Sets: Creation & Properties (unique, unordered) Set Operations (Union, Intersection, Difference) Mathematical Set Operations Subset & Superset Checks Frozen Sets & Immutability Practical Applications
Key Topics : Collections Module (namedtuple, Counter, defaultdict, deque) Iterators & Iteration Protocol Custom Iterators Generators & yield Statement Generator Expressions Memory Efficiency Concepts Lambda Functions Higher-Order Functions (map, filter, reduce) Functional Programming Concepts Generator Pipelines
Key Topics : Function Definition & Calling Function Parameters & Arguments Positional Arguments Keyword Arguments Default Arguments Arbitrary Positional Arguments (*args) Arbitrary Keyword Arguments (kwargs) Return Statements Multiple Return Values Local & Global Scope Global Keyword Usage Built-in Functions User-Defined Functions Lambda Functions (IIFE) Function Documentation (Docstrings) Recursive Functions
Key Topics : Introduction to Modules Types of Modules (Built-in, User-Defined, External) Importing Techniques Creating User-Defined Modules Common Built-in Modules (math, random, datetime, os, sys) Package Structure & Creation __init__.py File Purpose Nested Packages pip Package Manager Installing External Packages requirements.txt Management Popular External Packages (requests, pandas, numpy) Module Best Practices
Key Topics : File Operations Basics (CRUD) open() Function & File Modes Reading Files (read, readline, readlines) Writing Files (write, writelines) Append Mode Operations File Path Operations Directory/Folder Management (os, shutil) Working with CSV Files csv.reader & csv.writer csv.DictReader & csv.DictWriter Working with JSON Files JSON Operations (dump, dumps, load, loads) Data Serialization & Deserialization File Handling Best Practices
Key Topics : Exception Handling Fundamentals try-except-else-finally Blocks Catching Specific Exceptions Raising Exceptions Re-raising Exceptions Custom Exception Classes Built-in Exception Types Decorators Introduction Function Decorators Decorator with Arguments Multiple Decorators Class Decorators Practical Decorator Applications Generators Deep Dive Generator Expressions Infinite Generators Context Managers Custom Context Managers
Key Topics : OOP Fundamentals & Philosophy Classes & Objects Attributes (Instance & Class Variables) __init__ Constructor Method Understanding self Instance Methods Class Methods (@classmethod) Static Methods (@staticmethod) **Four Pillars of OOP:** **Encapsulation**: Access modifiers (public, protected, private) **Inheritance**: Single, Multi-level, Multiple inheritance **Abstraction**: Abstract classes, Abstract methods **Polymorphism**: Method overriding, Duck typing Method Overriding super() Function Special/Magic Methods (**str**, **repr**, **len**, etc.) Abstract Base Classes (ABC module) Real-World OOP Applications
02

SQL for AI & Data

Modern React with hooks, Redux Toolkit and routing, paired with PostgreSQL fundamentals through query optimization.
10 MODULES
WEEKS 2–4
Key Topics : Introduction to Databases & DBMS Relational Database Management Systems (RDBMS) ACID Properties (Atomicity, Consistency, Isolation, Durability) Introduction to PostgreSQL PostgreSQL Installation & Setup (Windows, Mac, Linux) PostgreSQL Tools: psql, pgAdmin 4 Database Objects (Databases, Schemas, Tables) Data Types: Numeric, Character, Date/Time, Boolean, Special types Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT Creating Databases and Tables INSERT Operations and Data Population Referential Integrity
Key Topics : SELECT Statement Basics Column Aliases and Expressions WHERE Clause and Filtering Comparison Operators (=, !=, >, <, >=, <=) Logical Operators (AND, OR, NOT) BETWEEN, IN, LIKE operators NULL handling (IS NULL, IS NOT NULL) ORDER BY (Sorting data) DISTINCT (Removing duplicates) LIMIT and OFFSET (Pagination) String Functions (UPPER, LOWER, CONCAT, SUBSTRING, etc.) Numeric Functions (ROUND, CEIL, FLOOR, ABS, etc.) Date and Time Functions (CURRENT_DATE, EXTRACT, DATE_TRUNC, etc.) Aggregate Functions (COUNT, SUM, AVG, MIN, MAX) GROUP BY and HAVING Window Functions (ROW_NUMBER, RANK, LAG, LEAD, etc.) JOIN Operations: INNER JOIN LEFT JOIN / RIGHT JOIN FULL OUTER JOIN CROSS JOIN SELF JOIN Multi-table Joins Join Optimization
Key Topics : Subqueries in WHERE, SELECT, FROM clauses Correlated Subqueries EXISTS and NOT EXISTS IN and NOT IN with subqueries Common Table Expressions (CTEs) Recursive CTEs for hierarchical data Multiple CTEs Set Operators: UNION and UNION ALL INTERSECT EXCEPT UPDATE Statements UPDATE with expressions UPDATE with JOIN DELETE Statements DELETE with subqueries TRUNCATE vs DELETE Transaction Management BEGIN, COMMIT, ROLLBACK Savepoints Transaction Isolation Levels Concurrency Control
Key Topics : ALTER TABLE Operations Adding, Modifying, Dropping Columns Managing Constraints Indexes and Performance Index Types (B-tree, Hash, GIN, GiST) Creating and Managing Indexes When to use indexes Views and Abstraction Creating Views Updatable Views Materialized Views Refreshing Materialized Views Stored Functions PL/pgSQL Programming Language Function Parameters and Return Types Control Structures (IF, CASE, LOOP) Functions Returning Tables Stored Procedures Procedure vs Function differences Exception Handling in PL/pgSQL Triggers BEFORE, AFTER, INSTEAD OF triggers Trigger Functions Audit Logging with Triggers Data Validation with Triggers Advisory Locks
Key Topics : Entity-Relationship (ER) Modeling Entities, Attributes, Relationships Relationship Types (1:1, 1:M, M:N) ER Diagrams Normalization Principles First Normal Form (1NF) Second Normal Form (2NF) Third Normal Form (3NF) Normalization Benefits and Trade-offs When to Denormalize Database Design Best Practices Naming Conventions Data Type Selection Primary Key Strategies Foreign Key Design Query Optimization EXPLAIN and EXPLAIN ANALYZE Reading Execution Plans Index Strategies Query Rewriting Techniques Performance Tuning Database Statistics (ANALYZE) VACUUM and Maintenance Connection Pooling Table Partitioning
03

Power BI for Data Analysis

Python from fundamentals through OOP, then FastAPI — async APIs with Pydantic validation, SQLAlchemy, and JWT auth.
15 MODULES
WEEKS 5–8
Key Topics : Business Intelligence fundamentals and modern analytics Power BI components and architecture Interface navigation and first report creation Understanding Desktop vs. Service capabilities
Key Topics : File, database, cloud, and web source connectivity Import vs. DirectQuery vs. Live Connection Data source settings and credential management Performance considerations for connection modes
Key Topics : Power Query interface and applied steps Data profiling and quality assessment Essential transformations: filtering, splitting, merging Reshaping: pivot, unpivot, grouping Combining queries: append and merge operations
Key Topics : Star schema vs. snowflake schema design Creating and managing table relationships Primary and foreign keys Hierarchies and date dimension tables Data model optimization strategies
Key Topics : Data visualization principles and chart selection Core visualizations: charts, tables, maps, KPIs Interactive elements: slicers, filters, bookmarks, drill-through Dashboard layout and mobile optimization Storytelling with data
Key Topics : DAX syntax and structure Calculated columns vs. measures Essential functions: aggregation, logical, text, date/time CALCULATE and FILTER functions Creating KPIs and business metrics
Key Topics : Time intelligence functions: YTD, MTD, QTD Prior period comparisons and growth rates Filter context vs. row context Variables and iterator functions DAX performance optimization
Key Topics : Custom visuals from AppSource Advanced chart types: waterfall, funnel, decomposition tree R and Python integration AI visuals: Key Influencers, Q&A, Smart Narratives Dynamic visuals with parameters
Key Topics : Publishing and workspace management Dashboards vs. reports Data refresh and gateway configuration Sharing strategies and Power BI apps Integration with Teams, SharePoint, Excel, PowerPoint
Key Topics : Power BI admin portal and tenant settings Row-Level Security (RLS) and Object-Level Security (OLS) Incremental refresh and aggregations Dataflows and deployment pipelines Performance optimization and capacity management Enterprise licensing models APIs and embedded analytics
04

PySpark

Production AI Engineer : Power BI for analytics, then Microsoft Fabric — OneLake, Lakehouse medallion architecture, Spark, real-time intelligence, and Copilot.
25 MODULES
WEEKS 9–14
Topics Covered: What Spark is and why it matters Spark architecture (driver, executors, cluster manager) Spark deployments (local vs cluster) SparkSession and basic application structure Lazy evaluation and lineage Transformations vs actions Narrow vs wide transformations
Topics Covered: Creating DataFrames (CSV/JSON/Parquet) Schema inference vs explicit schemas Column expressions (col, lit, when) select, withColumn, drop, cast filter/where, distinct, orderBy Handling nulls (dropna, fillna) String/date functions
Topics Covered: groupBy aggregations (count, sum, avg) Window functions (row_number, rank) Joins (inner/left/right/full/semi/anti) Broadcast joins and when to use them De-duplication patterns
Topics Covered: Temporary views and SQL queries SQL functions and UDFs (when to avoid) Explaining query plans Common optimization pitfalls
Topics Covered: Partitions vs files Repartition vs coalesce Caching and persistence levels Shuffles and skew Adaptive Query Execution (AQE) Common performance tuning checklist
Topics Covered: Parquet vs Delta basics Delta tables and ACID properties Writes (append/overwrite) MERGE (upsert) concept Time travel concept
Topics Covered: Building layered ETL (Bronze/Silver/Gold) Data quality checks and validation Incremental processing strategies Logging and error handling patterns
05

Databricks

The mathematical backbone behind every ML and DL model: linear algebra, probability, distributions, hypothesis testing, and applied statistics for ML.
5 MODULES
WEEK 15
Topics Covered: Workspace, notebooks, repos Clusters (interactive vs job) Compute types and cluster policies Access control basics (workspace vs data)
Topics Covered: Notebook fundamentals and best practices Parameters and widgets Version control with Git (Repos) Code organization patterns
Topics Covered: Running PySpark workloads in Databricks Optimizing Spark jobs on Databricks Libraries and dependency management Scheduling jobs and job parameters
Topics Covered: Delta Lake architecture Transaction log and ACID semantics OPTIMIZE and ZORDER Vacuum and retention Schema evolution and enforcement Delta Live Tables (overview)
Topics Covered: Catalog/schema/table structure Data access controls and permissions Lineage basics Secrets management overview Best practices for data governance
Topics Covered: Databricks Jobs (workflows) Task dependencies and retries Job clusters vs all-purpose clusters Monitoring runs and logs CI/CD patterns (overview)
Topics Covered: Databricks SQL warehouses Queries, dashboards, and alerts Connecting BI tools (Power BI/Tableau) Performance basics for SQL warehouses
Topics Covered: Structured Streaming basics Checkpointing and exactly-once concepts Common streaming patterns
Tools you'll master

40+ tools, one production capstone.

Not a shallow tour. You'll use every one of these in at least one graded exercise.

R
React 18
RT
Redux Toolkit
TS
TypeScript
V
Vite
Nd
Node.js
Py
Python
FA
FastAPI
SA
SQLAlchemy
Pg
PostgreSQL
M
MongoDB
PB
Power BI
MF
MS Fabric
Np
NumPy
Pd
Pandas
Sk
scikit-learn
TF
TensorFlow
PT
PyTorch
HF
Hugging Face
SM
spaCy
OAI
OpenAI
LC
LangChain
LG
LangGraph
LS
LangSmith
MC
MCP
VD
Vector DBs
D
Docker
K
Kubernetes
G
Git
GH
GitHub
aws
AWS
Az
Azure
C
Cursor AI
Real-time projects

You don't watch videos. You ship software.

Three full-production projects, each threaded through the entire curriculum. By the capstone, you've built the whole stack around them.

Hero project · weeks 3–12

LMS analytics platform

Ingest learner events, build transformation layers, and publish executive and academic dashboards with AI-generated insight summaries.

PySparkDatabricksPower BILangGraphPostgreSQL
View project →
Enterprise · weeks 6–11

HRMS data pipeline

Build secure ETL workflows for employee, payroll, and performance datasets with governed semantic models and decision-ready KPIs.

MS FabricDelta LakePower BIUnity Catalog
Real-time · weeks 8–12

CRM intelligence stream

Create near real-time customer analytics with streaming events, automated anomaly flags, and AI-assisted executive reporting.

Structured StreamingKQLPower BILangChain
Capstone · weeks 11–12

Your AI AI Engineer agent in a real partner org.

Pick a real partner data problem. Deploy a production data pipeline and an AI agent that explains metrics, detects risks, and accelerates business decisions.

2026: 220+ deployed76% → placement offers
See capstone gallery →
Your instructor

Taught by engineers who shipped agentic AI to production.

Not a career trainer. A practitioner who still ships code.

AS
Aarav Sharma
Lead Instructor · AI Engineer & AI
React · FastAPI · PyTorch · LangChain
"A 2026 full-stack engineer doesn't stop at React + an API. They train the model, deploy it behind FastAPI, wrap it in an agent, and ship the whole thing to a real org. That's what we build, every cohort."
10 yrs
FULL STACK
2,400+
LEARNERS
4.9 /5
RATING

Aarav started as a React engineer at an Indian unicorn before leading platform teams across three continents. He's shipped React + FastAPI products for a healthcare network with 80M users, trained NLP classifiers in production for a top-3 bank, and — most recently — deployed the first LangGraph agent into a Fortune-500 insurer's claims pipeline.

His cohorts get two things other programs don't give you: a real engineer who still ships code, and a curriculum rewritten every quarter to match what hiring managers actually ask about.

FAQ

Questions we actually get — answered honestly.

If the answer you need isn't here, book a 20-minute advisor call. No-slides, no-pitch — just your questions.

No. About 40% of our AI Engineer cohort comes from non-CS backgrounds — mechanical, electrical, and commerce. The first phase is foundations by design. What you need: consistency and around 12–15 hours/week.
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours, and roughly 5 hours of asynchronous project work. Weekends are optional office hours with the TA team.
Yes. Every student gets a dedicated placement advisor from week 8 onwards — not a helpdesk. They review your resume, redo your LinkedIn, mock-interview you, and make direct warm introductions to our 1,000+ hiring partners. We track individual outcomes, not cohort averages.
Full refund within 7 days of cohort start, no questions. Pro-rata refund through week 4 if the program isn't working for you. We'd rather refund than have an unhappy alum.
You actually build. Sections 6 (ML), 7 (DL/NLP), and 8 (Generative + Agentic AI) are hands-on — you'll train classifiers, build a RAG pipeline, ship a LangGraph workflow, and deploy your capstone agent into a real partner org. Nothing in the AI track is theory-only.
You get the Agent-Ready 2026 credential, graded on a 1–5 band with a public verification URL. It's co-branded with our partner ecosystem (Salesforce Partner + AI Engineer), and it names the specific capstone artifact you deployed. Recruiters can verify in 10 seconds.
All three. On-campus at our Hyderabad flagship; online cohorts on IST and PST; weekend cohorts for working professionals. Every format ships the same three projects and the same capstone.
We'd rather pause your cohort than push you through. You can freeze your seat for up to 90 days and rejoin the next cohort without paying again. TAs run catch-up sessions every Saturday for anyone more than one week behind.

Cohort 014 starts 14 May 2026.
40 seats. 12 already claimed.

Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real capstones from cohort 022.

₹89,000
₹1,20,000
25% off · EARLY BIRD
3 MONTHS · STARTS 14 MAY · 40 SEATS · 12 CLAIMED

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