SB
Spandana Bala
FullStack AI Engineer
Hyderabad · India
Now at · Infosys
FullStack · React · Next.js · Node · FastAPI · AI agents · MCP · Enrolling now
FullStack & AI Agents
A career-focused, code-first program: build strong TypeScript, React, Next.js, Node and FastAPI foundations, then build AI products with agents, RAG and MCP tools — streaming UIs, secure back ends, evals and guardrails, shipped to real users with coding agents at your side.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Edify Nuva centres
Where our FullStack & AI alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is the FullStack & AI Agents program?
A full-stack AI engineer builds the whole product — the interface people use, the back end and data behind it, and the agents, retrieval and tools that make it intelligent — and ships it safely to real users. Most full-stack courses end at a CRUD app; most AI courses end at a chatbot. This program ends only when you have shipped a deployed product with a streaming React front end, a secure API, agents with MCP tools and RAG, and evals, guardrails and observability behind it.
The complete delivery chain
Twelve links, one owner — end to end.
01FRONT END
- •HTML, CSS, Tailwind & TypeScript
- •React & Next.js App Router
- •Streaming chat & generative UI with the AI SDK
- •Testing, accessibility & performance
02BACK END & AI
- •Node / FastAPI APIs, auth & PostgreSQL
- •LLM engineering, structured outputs & tools
- •RAG with pgvector & embeddings
- •Agents with Claude Agent SDK, OpenAI Agents SDK & LangGraph
03SHIP
- •MCP servers & third-party integrations
- •Evals, guardrails & tracing
- •CI/CD, Docker & cloud deployment
- •Coding-agent workflows with Claude Code & Cursor
Full-stack development in 2026
Every product got an AI layer. Full-stack engineers build it end to end.
AI-native product UIsFront-end shift
Streaming chat, generative UI, tool-call rendering and human-in-the-loop controls built with Next.js and the Vercel AI SDK. React skills now include designing for non-deterministic responses.
Agent SDKs in TypeScript & PythonBuild layer
Claude Agent SDK, OpenAI Agents SDK, Vercel AI SDK agents and LangGraph turn a model into a looping, tool-using agent with state and handoffs — callable from any full-stack app.
MCP as the integration standardProtocol layer
Products expose their capabilities as MCP servers so Claude, ChatGPT, Cursor and other agents can use them; apps consume third-party MCP tools in return. Full-stack engineers build both sides.
Coding agents on the teamDeveloper workflow
Claude Code, Cursor and Codex write and test much of the code. The engineer's value shifts to specification, architecture, review and the evals that keep generated code honest.
Evals & observabilityReliability layer
Eval datasets, LLM-as-judge, regression suites and tracing with LangSmith, Langfuse or Braintrust — the difference between a demo and a product users trust.
Security & responsible AITrust layer
Prompt-injection defence, output filtering, permissions on tools, PII handling and the EU AI Act — the checklist enterprise customers now run before adopting an AI feature.
What this means for your career: full-stack roles now ask for AI features — chat, agents, RAG, MCP — alongside React, Node and databases; the differentiator is a deployed AI product with real users, evals and guardrails, not a CRUD portfolio.
Who should join
Built for people who want to build and ship AI products end to end.
CS / IT graduates & career switchers
Front-end or back-end developers going full stack
Full-stack developers adding AI
QA & support engineers moving to development
Founders & product builders
Designers who want to ship
Prior experience: none required — HTML, JavaScript, TypeScript and Python are taught from scratch. The program builds web and API foundations before LLM engineering, agents and production delivery.
What you will be able to do
Build the whole product — and ship it to real users.
Build modern front endsTypeScript, React, Next.js App Router, Tailwind, shadcn/ui, testing and performance.
Build AI interfacesStreaming chat, generative UI, tool-call rendering and human-in-the-loop with the Vercel AI SDK.
Build secure back endsNode or FastAPI APIs, auth, PostgreSQL, pgvector, queues and Docker.
Engineer LLM featuresPrompting, structured outputs, tool calling, RAG, memory and cost control.
Build agents and MCP toolsClaude Agent SDK, OpenAI Agents SDK and LangGraph agents; MCP servers exposing your product.
Ship with confidenceEvals, guardrails, tracing, CI/CD, cloud deployment and coding-agent workflows.
Course curriculum
Twelve sections. 60 modules. Web → React → APIs → LLMs → RAG → Agents → MCP → Ship.
01
Fundamentals of IT & AI
How the web, software and AI systems fit together — and where a full-stack engineer works in the 2026 stack.
Client-server, HTTP, REST and JSON
Browsers, DNS and hosting
Operating systems, terminals and Git
Cloud basics — compute, storage, networking
Frontier models — Claude, GPT, Gemini — and open weights
From chatbots to agents: tool use and reasoning
MCP, A2A and agent interoperability
AI engineering as part of full-stack work
Tokens, context windows and cost
Non-determinism and its product consequences
Structured outputs and tool calling at a glance
What LLMs can and cannot do in an app
VS Code, Node, Python and uv setup
Git and GitHub workflows
Claude Code and Cursor as pair programmers
Reviewing AI-generated code
Privacy, consent and PII in apps
Prompt injection and AI-specific risks
EU AI Act and NIST AI RMF overview
Secure-by-default habits
02
Foundations of Web — HTML, CSS & JavaScript
The front-end fundamentals every AI product still needs.
Document structure and semantic elements
Forms, inputs and accessibility
Media and metadata
SEO basics
Box model, Flexbox and Grid
Responsive design and media queries
Tailwind CSS
Design tokens and theming
ES modules, let / const, arrow functions
Arrays, objects and destructuring
Promises, async / await and fetch
Error handling and debugging
DOM manipulation and events
Local storage and web APIs
Streaming responses with fetch and SSE
Performance basics
Types, interfaces and generics
Typing API responses and LLM outputs
tsconfig and tooling
Migrating JavaScript to TypeScript
npm / pnpm and package management
Vite build tooling
Vitest and Playwright basics
Linting and formatting
03
Modern Frontend with React & Next.js
Build the interfaces AI products need — streaming, generative UI and real-time state.
Components, props and JSX
State, events and lists
Hooks — useState, useEffect, useRef
Component design
Context and reducers
TanStack Query and server state
React Hook Form and Zod validation
Optimistic updates
Routing, layouts and server components
Server actions and route handlers
Rendering strategies and caching
Deployment on Vercel
Vercel AI SDK and useChat
Streaming tokens and tool-call rendering
Generative UI components
Handling errors, retries and cancellation
shadcn/ui and Radix
Accessibility and keyboard navigation
Dark mode and theming
Storybook basics
Testing Library and Playwright
Core Web Vitals
Bundle analysis and lazy loading
Monitoring front-end errors
04
Backend with Node.js & TypeScript
APIs, auth and data — the backbone of any app, AI or not.
Node runtime and modules
REST APIs with Express or Fastify
Middleware, validation and errors
OpenAPI documentation
Relational modelling and SQL
Prisma or Drizzle ORM
Migrations and seeding
pgvector for embeddings
Sessions, JWT and OAuth
Auth.js, Clerk or Supabase Auth
Role-based access control
Securing AI endpoints
WebSockets and Server-Sent Events
Queues with BullMQ and Redis
Long-running agent jobs
Webhooks
Vitest and supertest
Rate limiting and CORS
Input validation and OWASP basics
Secrets management
05
Python & FastAPI for AI Services
The language of AI tooling — and the service layer agents run behind.
Syntax, types and collections
Functions, classes and modules
Virtual environments and uv
Python vs JavaScript idioms
Type hints and Pydantic models
asyncio and httpx
Concurrency for LLM calls
Testing with pytest
Routes, dependency injection and validation
Streaming responses and background tasks
Auth and rate limiting
OpenAPI and client generation
SQLAlchemy and Alembic
PostgreSQL and pgvector
Query performance
Data privacy and PII handling
Dockerfiles and compose
Deploying to Railway, Fly.io, AWS or GCP
Environment configuration
Health checks and logging
06
LLM Engineering
Work with models as an engineer — reliably, cheaply and safely.
Messages and Responses APIs
System prompts and multi-turn
Model selection — quality, cost, latency
Running open-weight models with Ollama
Instruction design and examples
Context engineering — what goes in the window
Prompt caching and extended thinking
Prompt versioning
JSON schema and Zod / Pydantic outputs
Function and tool calling patterns
Parallel tools and error handling
Computer use and browser tools (overview)
Image and PDF understanding
Speech-to-text and text-to-speech
Document extraction pipelines
Multimodal UI patterns
Token accounting and budgets
Caching, batching and routing
Streaming for perceived latency
When to fine-tune vs prompt vs RAG
07
RAG & Knowledge Features
Ground your product in its own data — and prove the retrieval works.
Embedding models
pgvector, Qdrant, Pinecone and Chroma
Similarity search and filtering
Indexing at scale
Parsing PDFs, HTML and office docs
Chunking strategies and metadata
Incremental ingestion
Permission-aware retrieval
Hybrid search and reranking
Query rewriting
Agentic RAG
Citations and grounding verification
Short-term vs long-term memory
User and session memory patterns
Summarisation
Privacy in memory systems
Retrieval metrics
RAG eval frameworks
Golden datasets
Iterating on quality
08
Agentic AI — Frameworks & Patterns
Build agents that finish the job, with the frameworks product teams use.
The agent loop — perceive, reason, act
ReAct, plan-and-execute and reflection
Single vs multi-agent
When not to build an agent
Agent loop, built-in tools and subagents
Hooks, skills and memory
Building a research and support agent
Deployment patterns
Agents, tools, handoffs and guardrails
Responses API
AI SDK agent loops in TypeScript
Choosing a framework
Graphs, state and persistence
Human-in-the-loop interrupts
Long-running and background agents
LangGraph Platform
Supervisor and swarm patterns
Handoffs and shared state
Approval gates and escalation
Failure handling
Showing plans, tools and progress
Confirmations and undo
Trust and transparency in UI
Feedback capture
09
MCP & Tool Engineering
Connect agents to your product and the world — safely and reusably.
Hosts, clients, servers, tools, resources and prompts
Transports and auth
The MCP ecosystem
Security model
MCP SDKs
Exposing your app's APIs as tools
Tool contracts agents understand
Testing and publishing
Idempotent, safe tool design
Error contracts and retries
Confirmation and approval patterns
Tool evaluation
Stripe, Slack, GitHub and Google Workspace tools
Salesforce and ServiceNow MCP servers
Webhooks and events
Permissions across boundaries
10
Evals, Guardrails & Observability
The discipline that separates demos from products.
What to measure and why
Golden datasets and rubrics
Offline vs online evaluation
Evals in CI
Judge design and calibration
Trajectory and tool-use evals
Braintrust, LangSmith, Langfuse and promptfoo
Regression suites
Input and output guardrails
Prompt injection and jailbreak defence
PII and content filtering
Permission boundaries for tools
OpenTelemetry for LLM apps
Traces and token accounting
Dashboards and alerts
Debugging agent failures
Attack taxonomies
Automated red-teaming
Abuse prevention and rate limiting
Incident response
11
DevOps, Deployment & AI-Native Development
Ship and run full-stack AI products — with coding agents as part of the team.
GitHub Actions pipelines
Vercel, Railway, Fly.io and AWS
Environments and secrets
Preview deployments
Docker and compose for full-stack apps
Managed databases and Redis
Infrastructure as code basics
Scaling and cost
Error tracking with Sentry
Product analytics with PostHog
AI feature metrics — usage, quality, cost
Feature flags
Claude Code workflows, CLAUDE.md and skills
Cursor agent mode
Spec-first and test-first with agents
Code review of AI-generated code
Repository tools and sandboxing
Plan, edit, run, test loop
Safety and evaluation
Where coding agents fit in a team
12
Capstone, Portfolio & Career
A production full-stack AI product with agents, evals and real users — verifiable by employers.
Next.js front end with streaming chat and generative UI
FastAPI or Node back end with auth, PostgreSQL and pgvector
Agents with Claude Agent SDK or LangGraph, MCP tools and RAG
Evals, guardrails, tracing and deployment with a public verification URL
Expose your product as an MCP server
Integrate a third-party service
Tool evaluation and safety
Demo in Claude and Cursor
GitHub portfolio with READMEs and demos
Resume rewrite around shipped products
Full-stack and AI interview practice — system design and live coding
Warm introductions to hiring partners
Tools you'll master
32+ FullStack & AI agent tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three portfolio projects and a partner capstone, each threaded through the entire curriculum — front end, back end, agents, MCP and evals all land in real deliverables.
Hero project
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
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
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
Your AI fullstack agent product in a controlled project environment.
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 deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Production-style capstoneCareer support included
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Edify Nuva · 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 class."
15 yrs
FULLSTACK
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Edify Nuva 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, Edify Nuva · 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 Edify Nuva, 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 Edify Nuva, 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 Edify Nuva grads.
Real feedback from talent leaders at AI-first product orgs and the firms hiring our FullStack & AI Agents graduates.
Edify Nuva 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+ Edify Nuva 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 Edify Nuva 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
Edify Nuva'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+ Edify Nuva 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. Edify Nuva 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+ Edify Nuva graduates for our agent products team. Strong TypeScript + Python depth, and they actually understand evals.
Geetha Pillai, Talent Acquisition Lead, Hugging Face
Edify Nuva 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.
Edify Nuva · 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 · Edify Nuva
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.
Job roles
Roles this program prepares you for.
Full-Stack Developer (AI products)
Build web applications with AI features end to end.
AI Application Engineer
Ship chat, agent and RAG features into products.
Frontend Engineer (React / Next.js)
AI-native interfaces with streaming and generative UI.
Backend Engineer (Node / FastAPI)
APIs, data and agent services behind AI products.
Agentic AI Engineer
Multi-agent systems with MCP tools and evals.
MCP / Integration Engineer
Expose products as MCP servers and integrate third-party tools.
Product Engineer
Own features from design to deployment in AI startups.
Founding Engineer
Build the first version of an AI product.
Solutions Engineer (AI)
Prototype and integrate AI features for customers.
Tech Lead / AI Architect (career path)
Grow toward leading full-stack AI teams.
What employers should see in your portfolio: that you can take an idea to a shipped AI product — a Next.js front end with streaming and generative UI, a secure Node or FastAPI back end on PostgreSQL and pgvector, agents with MCP tools and RAG, evals, guardrails and tracing, deployed and used by real people.
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 classes 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 8142998866
US desk
+1 256 388 7766
Hours
Mon–Sun · 7 AM–9 PM
Online class
Global
Weekend and evening FullStack & AI classes running on IST and PST. Every online class
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
Admissions
ENROLLING NOW
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. The opening modules cover JavaScript/TypeScript, Python, and the web platform from scratch. What you do need is consistency and regular practice.
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.
How is the learning workload structured?
The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.
Is placement support really 1:1, and which companies hire fullstack AI engineers?
Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. Edify Nuva does not guarantee an interview, offer, salary, employer, location, or timeline.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), 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 learners needing additional support, 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.
One million AI‑native professionals by 2027.
Let's put you in that number.
Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.
