SB
Spandana Bala
FullStack AI Engineer
Hyderabad · India
Now at · Infosys
Class 023 · Full AI Stack · Enrolling Now
Full AI Stack
+
AI Agents
Our most comprehensive AI-native program. Master React + FastAPI, PostgreSQL + Fabric, then ML, Deep Learning, NLP, and Generative + Agentic AI — ending with production systems on Kubernetes.
6–9mo
duration
75+
modules
4.8/5
class rating
100k+
enrolled
Where our Full AI Stack alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every Full AI Stack grad walks away with.
01
Agent-Ready skills
Build, deploy, and monitor AI agents that run production workflows on the full data + ML + GenAI stack — not chatbot toys.
02
A shipped project
A live React + FastAPI + LangGraph app on Kubernetes — monitored, observable, with a public verification URL.
03
Verifiable credential
2026 Agent-Ready rubric, graded 1–5 with a public verification URL recruiters can check in 30 seconds.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, resume rebuild, and warm intros into our 1,000+ hiring partners.
6–9 months, five phases
From "knows what an API is" to ships agentic AI in production.
MONTHS 1–2 · APP STACK
React · PostgreSQL · Python · FastAPI
- React 18 hooks, Redux Toolkit, routing
- PostgreSQL: schemas, joins, indexes, PL/pgSQL
- Python core + OOP + collections
- FastAPI: Pydantic, SQLAlchemy, JWT, RBAC
YOU SHIPA React + FastAPI + PostgreSQL CRUD app, deployed to Vercel + Render.
MONTHS 3–4 · DATA
Power BI · Microsoft Fabric · Lakehouse
- Power BI: Power Query, DAX, time intelligence
- Fabric OneLake · Lakehouse · Medallion arch.
- Data Factory pipelines + Spark notebooks
- Real-time intelligence + KQL
YOU SHIPAn end-to-end Bronze→Silver→Gold pipeline in Fabric, surfaced in a Power BI
report.
MONTHS 5–6 · ML + DL + NLP
Math · ML · Deep Learning · NLP
- Linear algebra, probability, hypothesis testing
- Regression, trees, SVM, ensembles, clustering
- ANNs, CNNs, RNNs/LSTMs in PyTorch
- NLP: embeddings, sentiment, NER, seq2seq
YOU SHIPA trained DL pipeline (CNN + LSTM) served as a FastAPI endpoint with drift
monitoring.
MONTHS 7–9 · GENAI + AGENTIC AI + CAPSTONE
Build an enterprise-grade agentic system, deployed to Kubernetes, in a partner org.
LangChain 1.0 + LangGraph 1.0, RAG with Pinecone or Qdrant, MCP-powered tools, HITL workflows on PostgreSQL + Redis, multi-agent systems via A2A. Deployed on Docker + Kubernetes with Prometheus, Grafana, and LangSmith observability. Walk out with a production system, a reference, and often, an offer letter.
Partner orgs
(2026)62
Projects
deployed320+
→ Placement
offers86%
Course curriculum
Eleven sections. 75+ modules. The full AI-native stack.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock for every Full Stack
developer and AI engineer — application lifecycle, Agile/Scrum, computing infrastructure,
AI/ML/Generative/Agentic AI fundamentals, and real-world digital systems. Sets the context for
everything that follows in the Full Stack + AI engineering stack.
Application fundamentals — what applications are, their types, web architecture
Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python,
Java, Node.js)
Database Systems — SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data
management
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
How each phase builds on the previous for systematic development of robust
applications
Understanding the SDLC is fundamental to managing complex software projects
Methodology Evolution — Waterfall vs Agile, the Agile mindset
Popular frameworks — Scrum, Kanban, Extreme Programming (XP)
Scrum Roles — Product Owner, Scrum Master, Development Team
Scrum Events — Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective
Scrum Artifacts — Product Backlog, Sprint Backlog, Increment deliverables
User Stories — Epics, Themes, Acceptance Criteria
Estimating user stories and managing backlogs with Google Sheets and Azure Boards
Transparent communication, progress tracking, collaborative planning across
distributed teams
CPU Technology — general-purpose computing, sequential operations, multi-core
parallel processing
GPU Technology — parallel processing for AI training, data processing, scientific
simulations
IaaS — Infrastructure as a Service: virtualised servers, storage, networking
PaaS — Platform as a Service: development and deployment environments without
infrastructure management
SaaS — Software as a Service: ready-to-use applications via web browser
Understanding fundamental computing technologies and cloud service models is
essential for navigating today's digital landscape
Machine Learning — algorithms that improve through experience, learning from data
patterns without explicit programming
Deep Learning — neural networks with multiple layers that process complex
patterns in large datasets
Generative AI — systems that create new content, from text to images, based on
learned patterns
Large Language Models — LLMs that process and generate human-like text, powering
chatbots, content creation, and translation
Image Generation — AI models creating original images from text descriptions
Customer Relationship Management — CRM systems centralising customer
interactions, sales pipelines, marketing campaigns
Human Resource Management Systems — HRMS solutions for recruitment, payroll,
performance management, employee records
Retail & E-Commerce — digital commerce platforms integrating inventory, payment
processing, customer analytics
Healthcare Applications — medical software managing patient records, appointment
scheduling, diagnostic support
Understanding real-world digital systems demonstrates how the technology stack
delivers measurable business value
02
Foundations of Web
Master the foundations every modern Full Stack developer must own — HTML5 for
structure, CSS3 for design, Bootstrap 5 for rapid prototyping, JavaScript (ES2024+) for behaviour,
TypeScript for type safety, and asynchronous programming for real-world API integration. The bedrock
for everything that follows — React, Node.js, and full-stack development all build on these
fundamentals.
Web Development & Architecture — introduction to web development and the
client-server architecture
Document Structure — HTML document structure and semantic HTML5 elements for
meaningful, well-organised markup
Text, Links & Media — working with text elements, hyperlinks, images, video, and
audio
Forms & Validation — forms and input elements with built-in HTML5 validation
Accessibility — A11y best practices, ARIA attributes, inclusive design from the
start
CSS fundamentals & selectors (Specificity)
Box Model, typography & colours
Layouts & positioning
CSS Variables & modern features
Flexbox for one-dimensional layouts
CSS Grid for two-dimensional layouts
Responsive Design & Media Queries
Transitions & Animations
Mastering CSS3 means understanding not just how to style elements, but how to
architect scalable, maintainable stylesheets that work beautifully across all screen sizes
Grid System — Bootstrap setup and the 12-column grid system for responsive
layouts at every breakpoint
Utility Classes — spacing, colours, display, and flexbox utilities for rapid
prototyping
UI Components — buttons, cards, alerts, badges, navigation bars, form components
JavaScript Components — modals, carousels, tooltips, dropdowns powered by
Bootstrap's JS plugin system
Theming Bootstrap with Sass variables and overrides to match any brand or design
system
Variables & Data Types — using let and const, understanding primitive types,
integrating JavaScript with HTML pages
Control Flow — conditional logic with if/else and switch
All loop types — for, while, do-while, for...in, for...of
Functions — function declarations, expressions, and arrow functions
Scope, hoisting, and how JavaScript executes code
Operators — arithmetic, comparison, and logical operators
Arrow Functions & IIFE
Callbacks & Higher-Order Functions
Closures & Function Scope
Recursion
Creating, accessing & modifying arrays
Mutating methods — push, pop, splice, sort
Non-mutating methods — slice, concat, join
Iteration — forEach, map, filter, reduce
Destructuring & Spread Operator
The array iteration methods — map, filter, and reduce — are among the most
powerful tools in JavaScript and essential for clean, functional-style code in both vanilla JS and
React
Promises — creation, chaining, error handling
async/await syntax for readable async code
Fetch API for HTTP requests
Working with REST APIs
Event loop & call stack — the JavaScript runtime model
TypeScript fundamentals — types, interfaces, generics
Why TypeScript — type safety, IDE support, refactoring confidence
Setting up TypeScript with React and Node.js projects
Migrating JavaScript codebases to TypeScript
In 2026, TypeScript is the production default for serious JavaScript projects —
every React and Node.js engineering team uses it
03
Modern Frontend with React.js
React.js is the world's most popular frontend library. From your first component
all the way through advanced patterns, state management with Redux Toolkit, and production
deployment. By the end you can architect, build, and deploy a complete React application — the
frontend half of your full-stack project.
Installing Node.js and npm
Creating a React app with create-react-app or Vite
Understanding the React project structure
Functional vs class components
JSX syntax and its advantages
Embedding expressions in JSX
JSX attributes — className and htmlFor
How React renders elements to the DOM
The Virtual DOM concept
Why React's reconciliation makes UIs fast and efficient
Passing data via Props
PropTypes for type checking
Default Props
useState for state management
Controlled vs Uncontrolled Components
Form handling with state
useEffect — side effects & data fetching
useRef — DOM access & persisting values
useMemo — performance optimisation
useCallback — memoising functions
useReducer — complex state logic
Custom Hooks — reusable logic
Custom Hooks let you extract and share stateful logic between components without
changing the component hierarchy
Inline styles with JSX
CSS Modules for local scoped styles
Styled-components (CSS-in-JS)
Creating Context with createContext
Provider and Consumer pattern
useContext hook for global state management without prop drilling
React Router v6 — BrowserRouter, Routes, Route
Link and NavLink components
useNavigate for programmatic navigation
Dynamic routes with useParams and nested routes with Outlet
Protected Routes with authentication
React Children and composition
Higher-Order Components (HOCs)
The Render Props pattern for maximum component reusability
Redux Toolkit store configuration
Slices and reducers
useDispatch and useSelector hooks
Async thunks for API calls
Axios integration
Error Boundaries for graceful error handling
React performance optimisation techniques
Code splitting with React.lazy and Suspense for faster load times
Deployment to Vercel — taking your React application from local development to a
live production URL
04
Node.js & MongoDB Backend
The backend section transforms you into a full-stack developer. Build robust,
secure, and scalable server-side applications with Node.js, Express.js, and MongoDB Atlas — the
production MERN stack used by millions of applications worldwide.
V8 Engine & Event Loop — how Node.js runs JavaScript
Blocking vs Non-Blocking I/O — the async-first model
CommonJS vs ES Modules — the two module systems
Node Version Manager (nvm) — managing Node versions
fs — File System operations
path — Path manipulation
os — Operating system info
http — Creating HTTP servers
Understanding package.json
npm vs yarn vs pnpm — package manager comparison
Custom modules and module exports
Environment variables with dotenv
Debugging Node.js applications
Express application setup
Routing basics — GET, POST, PUT, DELETE
Route parameters and query strings
Middleware concept and usage
Built-in middleware — express.json, express.static
Custom and error-handling middleware
Middleware execution order
Building RESTful endpoints
HTTP status codes (2xx, 4xx, 5xx)
Request/response objects
CORS configuration
Modular routes with Express Router
Using Postman and Thunder Client to test, debug, and document API endpoints
A well-designed REST API follows consistent conventions for URLs, HTTP methods,
and status codes
NoSQL architecture & features
Local install & Atlas Cloud
MongoDB Compass GUI
CRUD with MongoDB Shell
Defining schemas & schema types
Validation rules
Mongoose Models
Pre/Post Hooks (Middleware)
find(), findOne(), findById()
updateOne(), findByIdAndUpdate()
deleteOne(), findByIdAndDelete()
Creating & saving documents
Embedding vs Referencing — the core MongoDB design decision
One-to-One relationships
One-to-Many relationships
Many-to-Many relationships
Population (joining documents)
Virtual Properties
$match — filter documents
$group — group and aggregate
$project — shape output fields
$sort — order results
$lookup — join collections
Text Search in MongoDB
Indexes & performance optimisation
Custom validators, schema methods & statics, query helpers
Embedding vs Referencing is the most important MongoDB design decision — get it
right and your queries are blazing fast
Password hashing with bcrypt
JSON Web Tokens (JWT) — creating and verifying tokens
Building registration and login APIs
Protected routes with middleware
Role-Based Access Control (RBAC)
Input validation with express-validator
Rate limiting to prevent abuse
Helmet.js security headers
CORS configuration
File upload with Multer
Image storage (local and cloud)
Error handling strategies
Logging with Morgan and Winston
Environment configuration for dev, staging, and production
Deploying to Render, Railway, or Vercel with MongoDB Atlas production setup
Project — a complete production MERN app deployed to a public URL
05
Python for AI & Data
The parallel backend stack. Python is the dominant language for AI and data
engineering — and this section gives you the language fluency that makes the FastAPI section and the
GenAI section possible. From basic syntax through advanced OOP, decorators, generators, and context
managers, this section makes you production-fluent in the second language of full-stack 2026.
Python interpreter installation for Windows and Mac
Visual Studio Code IDE configuration for optimal development
Python's 35 essential keywords, identifiers, and naming conventions
Variables and memory management fundamentals
Simple and complex data types, type conversion and casting
Arithmetic, comparison, and logical operators
User input with input() function
Conditional statements — if, elif, else, match-case
while and for loops with range() function
break, continue, and pass statements
String definition, syntax rules, and immutability concept
Positive and negative indexing
Slicing with start:end:step notation
Concatenation and repetition operations
String formatting with f-strings and .format()
Case conversion — .upper(), .lower(), .title()
Search methods — .find(), .index(), .count()
Checking methods — .isalpha(), .isdigit(), .isspace()
Trimming — .strip(), .lstrip(), .rstrip()
Replacement and .split()/.join() operations
Lists — mutable sequences: creation, indexing, and slicing
Adding elements — .append(), .insert(), .extend()
Removing elements — .remove(), .pop(), .clear()
Sorting and reversing operations
List comprehensions for elegant data transformation
Tuples — immutable sequences: creation and basic operations
Tuple packing and unpacking
Performance advantages over lists and use cases for immutability
Dictionaries — key-value storage: creation, access, and operations
Methods — .keys(), .values(), .items()
Dictionary comprehensions
Nested dictionaries for structured data and fast key-based lookups
Sets — unique collections and the UUU properties (Unique, Unordered, Unindexed)
Mathematical operations — union, intersection, difference
Subset and superset checks
Frozen sets for immutable, hashable collections
Collections module — specialised container datatypes: namedtuple, Counter,
defaultdict, deque
Iteration protocol enabling custom iterators
Generators using yield for memory-efficient streaming
Generator expressions for concise creation
Generator pipelines chaining multiple operations efficiently
Lambda functions for anonymous functions
Higher-order functions — map(), filter(), reduce()
Function definition, parameters, return values
Default arguments, *args, **kwargs
Variable scope — local, enclosing, global, built-in (LEGB rule)
First-class functions and higher-order patterns
Recursion and recursive design patterns
Function annotations and type hints (Python 3.5+)
Documenting functions with docstrings (PEP 257)
Built-in modules that ship with Python
User-defined modules organising custom code
Importing techniques — import, from...import, aliasing
Packages organising related modules hierarchically with __init__.py
pip for package management
requirements.txt files for reproducible builds
Virtual environments
Common built-in modules — math, random, datetime, os, sys
Popular external packages — requests for HTTP, pandas for data analysis, numpy
for numerical computing
CRUD operations with open() function
File modes — r, w, a, b, x, +
Working with paths using pathlib
os and shutil modules for file operations and directory manipulation
Python's csv module — reader, writer, DictReader, DictWriter
Import and export operations for spreadsheet applications
JSON operations — dump(), dumps(), load(), loads()
Ideal for configuration files, API responses, structured data
Exception Handling — try-except-else-finally blocks, catching specific exceptions
Raising and re-raising exceptions, custom exception classes
Decorators — function decorators, decorators with arguments, multiple decorators,
class decorators
Practical decorator applications — logging, timing, authentication, caching
Generators deep dive — generator expressions, infinite generators, memory
efficiency for large datasets
Context Managers — with statement, custom context managers via __enter__ and
__exit__
These four patterns are what every senior Python role expects fluency in
Classes & Objects — classes define blueprints; objects represent instances
Methods — instance methods, @classmethod, @staticmethod
Special Methods — magic methods like __str__, __repr__, __len__, __init__
Instance variables vs class variables
Encapsulation — access modifiers (public, protected _, private __) control data
visibility
Inheritance — single, multi-level, and multiple inheritance with super() and
method overriding
Abstraction — abstract classes and methods (from abc module)
Polymorphism — method overriding and duck typing for flexible object interactions
06
SQL for AI & Data
The relational backbone of your Python backend. PostgreSQL is the de facto
open-source enterprise RDBMS — and this section gives you full mastery from foundations through
programming with PL/pgSQL, triggers, and query optimization. The data layer that powers your FastAPI
service.
Databases, DBMS, RDBMS — concepts and terminology
ACID properties — Atomicity, Consistency, Isolation, Durability
PostgreSQL setup and installation (local + cloud)
Connecting via psql, pgAdmin 4, and DBeaver
Data types — numeric, character, date/time, boolean, JSON, arrays
Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT
Creating, altering, and dropping tables
SELECT statements and column projection
WHERE clauses with operators and conditions
Built-in functions — string, numeric, date, conditional
Aggregates — COUNT, SUM, AVG, MIN, MAX
GROUP BY for aggregation across categories
HAVING for post-aggregation filtering
Window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
JOIN operations — INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF
Window functions are the single most underused SQL feature — master them and
you'll solve analytics problems in one query that others write 50 lines for
Subqueries — scalar, row, table subqueries
CTEs (Common Table Expressions) — WITH clauses for readable complex queries
Recursive CTEs for hierarchical data
Set operators — UNION, UNION ALL, INTERSECT, EXCEPT
DML — INSERT, UPDATE, DELETE patterns
Transactions — BEGIN, COMMIT, ROLLBACK, savepoints
Isolation levels and concurrency control
ALTER TABLE for schema evolution
Indexes — B-tree, Hash, GiST, GIN
Views — virtual tables, materialized views
Stored functions with CREATE FUNCTION
PL/pgSQL — variables, control structures, exception handling
Stored procedures vs functions
Triggers — BEFORE, AFTER, INSTEAD OF; row-level vs statement-level
ER (Entity-Relationship) modelling
Normalization — 1NF, 2NF, 3NF (and when to deliberately denormalize)
Design best practices for OLTP vs analytics workloads
Query plan analysis with EXPLAIN and EXPLAIN ANALYZE
Index strategies — selectivity, covering indexes, multi-column indexes
Statistics, VACUUM, ANALYZE
Partitioning for large tables
An index makes reads fast and writes slow — never add an index without measuring
both
07
Modern Python Framework FastAPI
The production Python API layer. FastAPI represents the next generation of
Python web frameworks — combining 3x performance (matching Node.js and Go), 100% type safety through
Pydantic, and automatic Swagger UI / ReDoc documentation. The production API layer for your AI
agents and the alternative to your Node.js/Express backend.
3x Performance — matches Node.js and Go performance
100% Type Safety — complete type hints for automatic validation
Auto Documentation — Swagger UI and ReDoc generated from code annotations
Environment Setup — virtual environment and Uvicorn ASGI server
First Endpoint — decorators @app.get, @app.post, @app.put, @app.delete
Run Server — launch development server with automatic reload
ASGI (Asynchronous Server Gateway Interface)
Path operations and HTTP method decorators
Built on Starlette (web framework) and Pydantic (data validation)
Path parameters with type hints — automatic conversion and validation
Query parameters with defaults — optional and required patterns
Pydantic models for request bodies — automatic validation, serialization
Type hints driving automatic validation
Error messages auto-generated from type definitions
The Pydantic-driven validation is what gives FastAPI its 100% type safety
guarantee — you don't write validation, the types are the validation
SQLAlchemy ORM with dependency injection
Session management for async and sync workloads
Connection pooling and lifecycle management
CRUD operations with transactional support
Alembic migrations for schema evolution
Repository pattern separating database from business logic
APIRouter organising code into modular components
Versioned APIs — /v1, /v2 patterns
Feature-based vs layer-based organisation
Repository pattern separating database logic from API logic
Environment variables with Pydantic Settings
Dependency injection for testability
Scalable project layout — service / schema / router / test layers
Password Security — Bcrypt hashing with industry-standard encryption
JWT Tokens — JSON Web Tokens with expiration for stateless authentication
OAuth2 Flow — OAuth2PasswordBearer dependency
Protected Routes — authentication dependencies; RBAC for permissions
ASGI server deployment with Uvicorn + Gunicorn
Docker containerisation
Health checks and readiness probes
Logging, monitoring, observability
Deploying to Render, Railway, Fly.io, AWS, or GCP
FastAPI is the obvious production framework for AI services in 2026
08
Generative AI & Agentic AI
The production AI engineering destination — and the culmination of the entire
programme. From the 70-year arc of AI history to deploying production RAG-powered agents — this
section builds the complete 2026 GenAI engineering stack: frontier models, prompt engineering,
multimodal AI, LLM APIs, vector databases, agentic frameworks, and the Model Context Protocol. The
named Coding Agent project lives here.
Narrow AI — image classifiers, speech recognition — the pre-2022 era
Generative AI — LLMs, image/video/audio generation — the post-2022 era unleashed
by ChatGPT
Agentic AI — Plan / Reason / Act / Learn loops, tool use — the post-2024 era of
autonomous systems
2022 inflection point — ChatGPT launch enters mainstream consciousness
2024 inflection point — AI systems begin planning, using tools, completing
multi-step tasks autonomously
What's coming 2026-2030 — increasingly capable reasoning models, deeper tool
integration
Multi-agent collaboration at scale and systems that learn continuously from
real-world feedback
LLM internals, the frontier model landscape, agent architecture, AI safety,
workstation setup
GPT-5.5 — The Autonomous Agent. Terminal-Bench 2.0 leader at 82.7%,
OSWorld-Verified at 78.7%
GPT-5.5 best for autonomous agents, computer use, terminal automation; 40% token
efficiency gain over GPT-5.4
Claude Opus 4.7 — The Precision Coder. Hybrid reasoning with extended thinking
mode
Claude Opus 4.7 — SWE-bench Pro leader at 64.3%, lowest hallucination rate at
36%, deepest native MCP support
Gemini 3.1 Pro — The Context Giant. Natively multimodal with 2M+ token context
window
Gemini 3.1 Pro — GPQA Diamond leader at 94.3%, ARC-AGI-2 leader at 77.1%, three
thinking levels
Open-source frontier — Llama 4 (Meta), DeepSeek, Mistral, Qwen
Intelligent Routing — the 2026 production pattern across multiple models
Microsoft Copilot Suite — Word, Excel, PowerPoint, Outlook, Teams integration
Copilot Studio — enterprise no-code agent building
Specialised tools — Perplexity (citation-grounded search), NotebookLM, ChatGPT
Codex
Fundamentals — anatomy of a great prompt: Context + Task + Examples + Format +
Constraints
Core Techniques — zero-shot, few-shot, Chain-of-Thought (CoT), ReAct,
Tree-of-Thought
System Prompts — persistent persona design, guardrails, extended thinking modes
Multimodal — vision, image generation, audio, video
Hallucination & Context — grounding, self-verification, citation prompting, token
budgeting
Domain & Library — 9 domain-specific patterns + versioned prompt library on
GitHub
Context Engineering — the 2026 frontier discipline that governs everything
entering the context window
Mastering context engineering is what separates junior AI users from senior AI
practitioners
Project — ship a 30+ prompt library on GitHub
Using ChatGPT, Claude, and Gemini for everyday productivity
Document drafting, summarisation, and editing workflows
Research with Perplexity and NotebookLM
Microsoft Copilot in Word, Excel, PowerPoint, Outlook, Teams
AI for code — GitHub Copilot, Cursor, Claude Code, ChatGPT Codex
Building personal AI workflows that compound over weeks
Vision capabilities — image understanding, OCR, chart reading
Image generation — Stable Diffusion, DALL-E, Midjourney, Imagen
Audio — speech-to-text (Whisper), text-to-speech, voice cloning
Video — generation (Sora, Runway), analysis (Gemini 3.1 Pro native video)
Native multimodal vs adapter-based multimodal
Cost and latency considerations for multimodal workloads
Bias in AI systems — sources, detection, mitigation
Hallucination — why it happens, how to reduce it, how to verify
Privacy considerations — PII, data residency
Security — prompt injection, jailbreaks, data exfiltration risks
Regulatory landscape — EU AI Act, India DPDP Act
Ethical AI principles — fairness, transparency, accountability, human oversight
Streamlit — rapid AI app prototyping in pure Python
Building chat interfaces, dashboards, demos
Session state, callbacks, reactive patterns
FastAPI — production-grade Python API for AI workloads
Async endpoints for high-throughput AI
Deploying to Render, Railway, Vercel
Build and deploy a Streamlit + FastAPI chatbot connecting to OpenAI / Anthropic /
Google LLMs
LLM APIs in production — OpenAI, Anthropic, Google GenAI, DeepSeek Python SDKs
API patterns — completions, chat, streaming, function calling, structured outputs
Rate limits, retries, exponential backoff, cost tracking and observability
Function calling & structured outputs — the 2026 production pattern for reliable
JSON
Pydantic-validated structured outputs for type-safe AI
Embeddings — OpenAI text-embedding-3-large, Voyage, Cohere embedding models
Vector databases — ChromaDB (local/dev), Pinecone (managed), Qdrant
(open-source), pgvector (PostgreSQL)
Indexing strategies — HNSW, IVF
RAG pipeline — Chunk → Embed → Index → Retrieve → Augment → Generate
Chunking strategies — fixed-size, semantic, hierarchical
Hybrid search (BM25 + embeddings) and re-ranking with cross-encoders
Agentic RAG — self-improving retrieval where the agent decides if it has enough
information
Multi-step retrieval, MCP-enhanced RAG
Project — Production RAG App with hybrid search, re-ranking, React frontend,
FastAPI backend, observability, deployed publicly
LangGraph 1.0 — complex stateful workflows, graph-based state machines, the
production default
Human-in-the-loop checkpoints and LangSmith observability
Claude Agent SDK — powers Claude Code, deepest MCP integration, extended thinking
CrewAI — role-based multi-agent crews, fastest prototyping, native MCP + A2A
support
Semantic Kernel / Microsoft Agent Framework — enterprise .NET stacks, AutoGen +
Semantic Kernel merged
Pydantic AI — type-safe Python, validation-first agent design
ReAct (Reasoning + Acting) pattern
Plan-and-Execute and Reflection loops
Multi-agent collaboration — supervisor, swarm, debate patterns
Human-in-the-loop checkpoints
Production agents are 90% about state management, observability, and
human-in-the-loop — the LLM is the easy part
MCP — the open standard for connecting agents to tools, data, and systems
Proposed by Anthropic in late 2024, now stewarded by the Linux Foundation
200+ server implementations and 97M+ monthly SDK downloads
Adoption across Anthropic, OpenAI, Google, Microsoft, AWS, and 50+ partners
Write the integration once, every agent uses it
Build an MCP server exposing tools and resources with authentication
Connect LangGraph agents to multiple MCP servers via adapters
Use Claude Agent SDK's deepest native MCP integration
Connect CrewAI via crewai-tools[mcp] and build MCP-enhanced RAG pipelines
A2A Protocol — Google-led agent-to-agent communication standard with 50+ launch
partners including Salesforce, SAP, Anthropic, Cisco
Linux Foundation governance
Three A2A state management levels — session-level, agent-level, task-level
(TaskStore)
CODING AGENT CAPSTONE — multi-agent coding system using LangGraph + Claude Agent
SDK with MCP servers exposing codebase, PostgreSQL, MongoDB, and external APIs
React frontend, FastAPI backend, observability via LangSmith, public verification
URL with the 2026 Agent-Ready rubric — the named project for the entire Full Stack & AI Agents
programme
Tools you'll master
32+ FullStack & AI agent tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project · weeks 3–12
Production agentic SaaS — React app + LangGraph backend + MCP
Build an end-to-end fullstack AI agent product — React 19 frontend, FastAPI + LangGraph backend, MCP server, evals — shipped to real users with cost, safety, and observability dialled in.
01Live React 19 + Next.js app on Vercel with shadcn/ui, real-time streaming chat UI, optimistic state, and full a11y/keyboard nav.
02FastAPI agent backend wired to a LangGraph supervisor topology, Postgres+Pinecone storage, OpenTelemetry, and replay-capable state.
03MCP server that exposes your agents as tools to Claude/ChatGPT desktop, with auth, scopes, rate-limit, and audit logging.
04Eval + safety harness — golden dataset, regression suite on every PR, hallucination + cost guardrails on a public dashboard.
Outcome: 5,000+ users on staging
p95 chat latency: <1.2s
Reviewer: Senior staff engineer panel
ReactFastAPILangGraphMCPVercel
Enterprise · weeks 6–11
Agent admin + observability dashboard
Ship a React/TanStack Query admin console — agent run history, eval scoreboard, prompt diff viewer, cost & latency analytics — backed by FastAPI + ClickHouse.
ReactTanStackClickHouseOpenTelemetry
Real-time · weeks 8–12
Real-time multi-user agent collab app
Build a real-time collaborative agent workspace — Yjs CRDT, WebSocket streaming from a LangGraph backend, presence + cursors, auth + tenancy.
YjsWebSocketLangGraphNext.js
Project · weeks 11–12
Your AI fullstack agent product in a real partner org.
Pick a real partner workflow. Ship a production fullstack agent product — React app, FastAPI + LangGraph backend, MCP server, evals — to a partner team that's running it for real users.
Download the real world project
Full scope, sample partner orgs, weekly milestones, and grading rubric — PDF, 14 pages.
2026: 220+ deployed76% → placement offers
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Lync · FullStack & AI Architect
React · Next.js · FastAPI · LangGraph · MCP · Evals
"FullStack today means React on the front, FastAPI + LangGraph on the back, and an MCP server tying your agents into the tools real users live in. That's the bar I teach to, every cohort."
15 yrs
FULLSTACK
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of fullstack platform architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led product engineering for React/Next.js apps, FastAPI services, and data platforms at Fortune-500 scale. Most recently he architected production fullstack agent products on top of LangGraph and MCP that replaced traditional SaaS surfaces with autonomous workflows.
His classes get you two things other programs don't give you: a founding architect who's still shipping production fullstack + AI from inside the Fortune 500, and a curriculum rewritten every release — so when hiring managers ask about React 19 server components, LangGraph supervisors, MCP auth, or eval pipelines, you've already built it. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · FullStack & Agent Platform Lead
React · Next.js · FastAPI · LangGraph · MCP · Vercel · Evals
"Shipping a fullstack agent product to real users is where prototypes die — auth,
tenancy, streaming, evals, cost guardrails, and an MCP surface that doesn't fall over at 3 AM. That's
what I teach, end to end."
10 yrs
FULLSTACK
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the FullStack & Agent Platform practice. After 8 years building production React + Node + Python platforms, he stepped into the Chief Technologist seat to wire LangGraph, MCP, and evals into the way product teams actually ship — agent backends with replayable state, React frontends with streaming UX, and observability dashboards that on-call engineers don't fight with.
His agent-platform modules are built from real production post-mortems, not slide decks. Expect to leave with working FastAPI + LangGraph backends, a React/Next.js reference app, an MCP server with auth and audit, and an eval harness you can stake a release on. Ten years at Digital Lync, eight of them shipping fullstack in production — Hyderabad-based, hands-on, and known for the unglamorous parts of agent products that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What FullStack + AI employers say about Digital Lync grads.
Real feedback from talent leaders at AI-first product orgs and the firms hiring our FullStack + AI Agents graduates.
Digital Lync grads ramp 40% faster on React + FastAPI agent products than typical hires. Best fullstack AI pipeline in India.
Aakash Mehta, AI Partner Programme Lead, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for Next.js + LangGraph agent practices.
Anita Sharma, Senior Manager, Deloitte
The fullstack programme is comprehensive — React, FastAPI, plus MCP servers and eval harnesses. Grads come pre-trained for production agent products.
Rahul Bhatt, Solutions Lead, Anthropic Partner
Their React 19 + FastAPI track produces engineers who ship streaming LangGraph endpoints to production on day one. Genuinely rare.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the MCP + evals layer baked into the fullstack track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their LangGraph + FastAPI prep is rigorous, and the project agent product deployed on Vercel is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's fullstack AI grads ship production agent features twice as fast in the first 90 days. Our internal metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best React/Next.js + LangGraph pipeline we've sourced from in India. Their projects are production agent products, not toy demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong Next.js App Router and FastAPI foundations. Their fullstack AI grads need almost zero ramp time on enterprise agent engagements.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our React + watsonx and LangGraph platform teams. Strong fundamentals, sharp on the eval stack.
Sanjay Verma, Talent Director, IBM
MCP servers + observability and cost guardrails is exactly the talent gap we've been struggling to close. Digital Lync is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their fullstack track delivers engineers who navigate React server components, FastAPI, and LangGraph supervisors on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our agent products team. Strong TypeScript + Python depth, and they actually understand evals.
Geetha Pillai, Talent Acquisition Lead, Hugging Face
Digital Lync grads who blend Next.js with LangGraph + MCP land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, LangChain
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Lync · Institute Certificate
Agent‑Ready FullStack Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a fullstack agent
product — React/Next.js app, FastAPI + LangGraph backend, MCP server, and eval harness — graded against
the 2026 Agent‑Ready rubric.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with our partner ecosystem and mapped to LangChain Academy badges, AWS
Solutions Architect, and Vercel certifications — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your project project name, the partner org, and a link to
the deployed React app + FastAPI/LangGraph backend + MCP server + eval harness — proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: React/Next.js architecture, FastAPI design,
LangGraph orchestration, MCP integration, evals, and cost guardrails. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
04Job placement support
Your first FullStack + AI offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into AI-first product orgs and
services that staff fullstack agent teams. Our placement team works your search like an account, not a helpdesk.
01 / GITHUB PROFILE
A portfolio, not a graveyard.
Guidance on building a GitHub that showcases your React/Next.js app, FastAPI + LangGraph backend, MCP server, eval harness, and a working production link — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the fullstack agent product you shipped (React, FastAPI, LangGraph, MCP, evals) and the business outcome. Reviewed by engineers who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into AI-first SaaS & product orgs — Microsoft, Anthropic / OpenAI partners, Hugging Face, LangChain, Databricks, Snowflake, Stripe, Vercel, Linear, Notion, Razorpay, Freshworks, Zoho, plus services that staff fullstack agent teams (Deloitte, Accenture, Cognizant). You leave with recruiter contacts, not a generic "good luck."
FullStack + AI alumni
Hundreds of fullstack AI careers launched — here are eight.
NV
Naveen Vedala
Senior FullStack Engineer (Agent Products)
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
LangGraph Backend Lead
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Staff FullStack Engineer (AI)
Seattle · United States
Now at · Microsoft
MM
Mujahed Mohammed
Principal Engineer (Agent Platforms)
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Agent UX Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Lead React Engineer (AI)
New York · United States
Now at · Vercel
RD
Rahul Dhamma
AI Product Engineer
Hyderabad · India
Now at · Cognizant
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online FullStack + AI cohorts running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 90003 29956
US desk
+1 858 666 6719
Hours
Mon–Sat · 9am–9pm
Online class
Global
Weekend and evening FullStack + AI cohorts running on IST and PST. Every online cohort
ships the same React app + FastAPI/LangGraph backend + MCP server + eval harness project as the
on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Next class
25 May 2026
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the stack, evals, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior fullstack experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA — and zero React or Python exposure is assumed. Weeks 1–2 cover JavaScript/TypeScript, Python, and the web platform from scratch. What you do need: consistency and 12–15 hours a week.
Will I actually ship a production app, or only build toy demos?
You ship a real one. Every learner deploys a project fullstack agent product — a React/Next.js app, a FastAPI + LangGraph backend, an MCP server with auth, and an eval harness — to a live URL on Vercel + a Python host. Every lab, project, and the project are live artifacts you can demo to recruiters, not slide decks.
Which framework + AI stack will I use?
Frontend: React 19, Next.js App Router, TypeScript, Tailwind, server components, streaming UI. Backend: FastAPI, Python, async I/O, Postgres, Redis. AI layer: LangGraph for agent orchestration, MCP servers for tools, evals + observability, plus cost & safety guardrails.
Will I learn evals, observability, and cost guardrails?
Yes — they're a first-class section, not a footnote. You build an eval harness for your agent flows, wire tracing + observability dashboards, and ship cost & safety guardrails (token budgets, rate limits, prompt-injection defenses) before you're allowed to call the project done.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours building on your own React + FastAPI repo, and ~5 hours of project work. Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire fullstack AI engineers?
Yes — a dedicated placement advisor from week 8, not a helpdesk. Hiring partners include Microsoft, Anthropic / OpenAI partners, Hugging Face, LangChain, Databricks, Snowflake, Stripe, Vercel, Linear, Notion, Razorpay, Freshworks, Zoho — plus services that staff fullstack agent teams (Deloitte, Accenture, Cognizant). Resume, LinkedIn, mock interviews, and warm intros are individual.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST cohorts), and a weekend track for working professionals. Every format ships the same three projects and the same fullstack agent project — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for anyone more than a week behind, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
Class FAS-021 starts 25 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 projects from class FAS-020.
