SB
Spandana Bala
Python AI Engineer
Hyderabad · India
Now at · Microsoft
Class 022 · Engineering Track · Enrolling Now
Full Stack Development
+ AI Agents
Become an AI-Native full-stack engineer. Master React + Node + Python with Django and FastAPI, MongoDB and PostgreSQL, then build the Generative and Agentic AI layer that lives inside your app.
3mo
duration
50+
modules
4.7/5
class rating
100k+
enrolled
Where our Full Stack alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every Full Stack grad walks away with.
01
Polyglot full-stack fluency
React + TypeScript on the front, Node + Python (Django, FastAPI) on the back, SQL and NoSQL data tiers — all wired with REST.
02
Three shipped projects
An LMS, an HRMS, and a CRM — each a different stack slice, all live with CI/CD running on every push.
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.
12 weeks, four phases
The path from "can write a for-loop" to ships full-stack apps with AI agents.
WEEKS 1–3 · FOUNDATIONS
Web + JS + TypeScript
- How apps, SDLC, Agile & Scrum work
- Semantic HTML5, modern CSS, Bootstrap
- JavaScript ES6+ — async, fetch, DOM
- TypeScript: types, generics, interfaces
YOU SHIPA typed, responsive marketing dashboard that consumes a public REST API.
WEEKS 4–6 · FRONTEND + NODE
React · Node · MongoDB
- React 18 hooks, Context, Redux Toolkit
- Node + Express REST APIs
- MongoDB + Mongoose schemas
- JWT auth, file uploads, Render deploy
YOU SHIPA full MERN app — React front end, Express API, MongoDB Atlas, deployed on Render + Vercel.
WEEKS 7–9 · PYTHON BACKEND
Python · Django · FastAPI · SQL
- Python core: OOP, decorators, generators
- PostgreSQL: queries, joins, design
- Django ORM, DRF, JWT auth
- FastAPI async APIs + Pydantic
YOU SHIPA Django + DRF HRMS module, plus a FastAPI async micro-service running side-by-side.
WEEKS 10–12 · AGENTIC AI + CAPSTONE
Build a LangGraph agent into a full-stack app, in a real partner org.
Generative AI, RAG with vector DBs, LangChain 1.0, LangGraph workflows, MCP-powered tools — all wired into your project. Ship a complete React + Python app with an embedded AI agent into a partner environment. Walk out with a production artifact, a reference, and often, an offer letter.
Partner orgs (2026)52
Projects deployed240+
→ Placement offers82%
Course curriculum
Nine sections. 52+ modules. Every one maps to something you'll ship.
01
Fundamentals of IT & AI
The ground floor: how modern applications work, how teams ship them with Agile, where compute & cloud fit, and how AI plugs into the 2026 stack.
What is an Application & types
Web application fundamentals
Frontend, backend, database tiers
SDLC phases end-to-end
SDLC methodologies compared
Waterfall vs Agile mindset
Scrum roles, events, artifacts
User stories, epics, estimation
Backlog management on a real board
STACKAzure BoardsJiraGoogle Sheets
CPU vs GPU — when each matters
Cloud computing concepts
IaaS · PaaS · SaaS service models
Mapping local dev → production cloud
What is AI & how it works
ML & Deep Learning fundamentals
Large Language Models & image models
Generative AI in everyday workflows
CRM systems
HRMS systems
Retail & e-commerce
Healthcare apps
02
Foundations of Web — HTML, CSS, JS, TS
An opinionated, modern web foundation. Semantic HTML5, real CSS layout systems, JavaScript through ES2022+, and TypeScript for production-grade safety.
Web dev & client-server architecture
Semantic HTML5 document structure
Text, links, media elements
Lists & tables
Forms, inputs, validation
Accessibility best practices
Selectors & specificity
Box model, typography, colors
Flexbox & CSS Grid
Responsive design & media queries
Transitions & animations
CSS variables & modern features
STACKCSS3FlexboxGrid
Setup & 12-column grid
Utility classes
Buttons, cards, alerts, badges
Modals, carousels, tooltips
Customization & theming
STACKBootstrap 5
Variables (let, const) & data types
Operators & control flow
Loops & iteration
Functions, scope, hoisting
Arrow functions & IIFE
Callbacks & higher-order functions
Closures & recursion
Array iteration: map, filter, reduce
Destructuring & spread operator
Object literals & methods
Object destructuring & spread
Template literals
Symbols & iterators
DOM tree & selecting elements
Manipulating content & styles
Event handling & delegation
Form validation
Sync vs async & the event loop
Promises & chaining
async/await & error handling
Fetch API for HTTP requests
LocalStorage & SessionStorage
Type annotations & inference
Arrays, tuples, objects
Interfaces & function types
Union, intersection, narrowing
Type aliases & enums
STACKTypeScripttsc
Classes & access modifiers
Generics & constraints
Utility types (Partial, Pick, Omit)
Mapped & conditional types
03
React JS Frontend Framework
React the way it's actually written in 2026. Functional components, hooks-first state, Context, Redux Toolkit, real routing, real forms, and Vercel deployment.
What is React, why React
Vite / create-react-app setup
Functional vs class components
JSX syntax & expressions
Rendering elements
STACKReact 18Vitenpm
Props & PropTypes
useState & re-rendering
Event handling
Controlled vs uncontrolled forms
useEffect & data fetching
useRef, useMemo, useCallback
useReducer for complex state
Custom hooks
Rules of hooks
CSS Modules & Styled Components
Context API for global state
React Router v6
Dynamic & nested routes
Protected routes
STACKReact Routerstyled-components
Higher-Order Components & Render Props
Redux Toolkit: store, slices, thunks
Axios integration
Code splitting & React.lazy
Vercel deployment
STACKRedux ToolkitAxiosVercel
04
Node.js & MongoDB
A real Node back end — not a 30-line Express tutorial. Express middleware, MongoDB schema design, JWT auth, and a production deployment pipeline.
V8 engine & the event loop
Blocking vs non-blocking I/O
npm vs yarn vs pnpm
CommonJS vs ES Modules
Built-in modules: fs, path, http
Environment variables with dotenv
STACKNode.jsnpmnvm
Routing: GET, POST, PUT, DELETE
Route params & query strings
Built-in & custom middleware
Error handling middleware
CORS & modular routers
Postman / Thunder Client testing
STACKExpressPostman
NoSQL fundamentals
MongoDB Atlas + Compass
CRUD with mongoose
Schemas, types & validation
Pre/post hooks
STACKMongoDBMongooseAtlas
Embedding vs referencing
1:1, 1:M, M:N relationships
Population & virtuals
Aggregation pipelines, $lookup
Indexes & performance
bcrypt password hashing
JWT auth & protected routes
Role-based access control
Helmet, rate limiting, validation
File uploads with Multer
Deploy to Render / Railway
STACKJWTbcryptRender
05
Python for FullStack
Python from variables to OOP and beyond. Strings, collections, iterators, decorators, generators, exception handling, and a strong OOP base — the prerequisite for Django, FastAPI, and AI work.
Setup, IDE, Python interpreter
Syntax, identifiers, variables
Data types & type conversion
Operators & conditionals
Loops & control flow
STACKPython 3.12VS Code
Indexing & slicing
Concatenation & repetition
f-strings & format()
Search, replace, split, join
Immutability & alignment
List creation, indexing, slicing
append, insert, extend, remove
Sorting, reversing, comprehensions
Tuples & unpacking
Dictionary CRUD & comprehensions
Nested dictionaries
Set operations & math
Frozen sets
namedtuple, Counter, defaultdict, deque
Custom iterators
Generators & yield
Lambda & map / filter / reduce
Positional, keyword, default args
*args & **kwargs
Local & global scope
Lambda & recursion
Docstrings
Built-in vs user vs external modules
Importing techniques
Package structure & __init__.py
pip & requirements.txt
requests, pandas, numpy
File modes & CRUD
os & shutil for paths
CSV reader/writer + DictReader
JSON dump/load & serialization
try/except/else/finally
Custom exceptions
Decorators & class decorators
Generator expressions
Context managers
Classes, objects, self
@classmethod, @staticmethod
Encapsulation, inheritance, polymorphism
Abstract base classes
Magic methods
06
SQL for AI & FullStack
PostgreSQL the way real engineers use it. Schema design, complex queries, transactions, indexes, PL/pgSQL functions, and execution-plan reading — the toolkit you'll use in Django, FastAPI, and pgvector.
RDBMS & ACID
PostgreSQL install & pgAdmin
Data types & constraints
Tables, schemas, referential integrity
STACKPostgreSQLpgAdmin
SELECT, WHERE, ORDER BY, LIMIT
Aggregates, GROUP BY, HAVING
Window functions: ROW_NUMBER, RANK, LAG
All JOINs incl. SELF JOIN
Subqueries & correlated subqueries
CTEs & recursive CTEs
UNION, INTERSECT, EXCEPT
UPDATE / DELETE with JOIN
Transactions & isolation levels
Indexes: B-tree, GIN, GiST
Views & materialized views
PL/pgSQL functions & procedures
Triggers & audit logging
ER modeling & normalization (1NF–3NF)
When to denormalize
EXPLAIN & execution plans
VACUUM, ANALYZE, partitioning
07
Django Framework
Django the way it's built in production. MVT, the ORM, class-based views, authentication flows, and a full Django REST Framework API with JWT and Swagger docs.
Why Django, MVT architecture
django-admin startproject & startapp
URL routing & first view
Django admin panel
Settings configuration
STACKDjango 5Python 3.12
Defining models & field options
makemigrations & migrate
CRUD with the ORM
QuerySet methods & relationships
ForeignKey, M2M, OneToOne
Function-based views
Template inheritance & tags
Static files & media
Django Forms & ModelForm
CSRF protection & file uploads
CreateView, ListView, DetailView
UpdateView, DeleteView
Built-in auth & user model
Login/logout, password reset
LoginRequiredMixin, sessions
Serializer vs ModelSerializer
@api_view & APIView
Generic views & ViewSets
JWT & Token auth
Pagination, filtering, Swagger docs
STACKDRFJWTSwagger
08
FastAPI Framework
FastAPI for the parts of your stack that need to be fast and async — AI gateways, integration services, real-time pipes. Pydantic validation, SQLAlchemy ORM, OAuth2, and JWT.
Flask vs Django vs FastAPI
Why FastAPI: async, validation, docs
Virtualenv + uvicorn setup
Path operations: GET, POST, PUT, DELETE
Auto Swagger & ReDoc
STACKFastAPIUvicornPydantic
Path & query parameter validation
Pydantic BaseModel classes
Automatic data validation
Nested & optional models
Response models
SQLAlchemy setup & models
DB session via dependency injection
Schemas vs Models
CRUD operations
Alembic migrations
STACKSQLAlchemyAlembic
APIRouter for modular code
Prefixes, tags, status codes
HTTPException & custom handlers
Repository pattern
CORS & environment variables
Bcrypt password hashing
OAuth2 password flow
JWT access tokens & expiration
Protected routes & current user
Role-based access control
STACKOAuth2JWTbcrypt
09
Generative AI & Agentic AI
The 2026 layer. LLM APIs, prompt & context engineering, RAG with vector DBs, LangChain 1.0, LangGraph workflows, MCP, and a fully observable agent inside your project app.
LLM fundamentals & transformers
Comparing GPT, Claude, Gemini, DeepSeek
Tokenization & cost optimization
Model selection by use case
Zero-shot, few-shot, chain-of-thought
Reasoning mode optimization
Reducing hallucinations
Multimodal prompting
OpenAI, Anthropic, Google, DeepSeek
create_agent abstraction
Middleware systems
Streaming, batching, function calling
STACKLangChain 1.0OpenAIAnthropic
ChromaDB, Pinecone, Qdrant
Production RAG pipelines
Agentic & self-improving retrieval
MCP-Enhanced RAG
Hybrid (semantic + keyword) search
STACKPineconeQdrantpgvector
Streamlit & Gradio interfaces
LangGraph Platform deployment
Cost optimization & rate limiting
EU AI Act compliance basics
Monitoring & observability
Plan, reason, act fundamentals
LangChain 1.0 Agents + middleware
Model Context Protocol (MCP)
Tool integration patterns
Graph-based workflow logic
State management
Node caching for dev
Pre/post hooks for guardrails
STACKLangGraphLangSmith
Parallel execution with deferred nodes
Conditional routing
Iterative refinement loops
Type-safe streaming
Quality-gated content generation
Durable state with PostgreSQL/Redis
HITL implementations
Multi-day workflows
Restart & failure recovery
Multi-agent system design
Google A2A protocol
LangSmith observability
MCP security & prompt injection
Compliance & agent guardrails
Tools you'll master
32+ Python & AI agent tools, one production project.
Not a shallow tour. You'll use every one of these in at least one graded exercise.
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 multi-agent backend on LangGraph + FastAPI
Build an end-to-end Python + AI agent backend — typed FastAPI service, a LangGraph multi-agent topology, a hybrid RAG layer, and an MCP server exposing your agents as tools to Claude and ChatGPT desktop.
01Live FastAPI service with Pydantic-typed contracts, async DB access, streaming SSE responses, and OpenTelemetry-instrumented routes.
02LangGraph multi-agent topology — supervisor + specialist nodes with tool calls, retries, fallback models, and replay-capable state.
03RAG layer with hybrid search across Postgres + Pinecone, evaluation harness using LangSmith golden datasets.
04MCP server exposing your agents as tools to Claude/ChatGPT desktop with auth, rate-limit, and observability dashboards.
Outcome: ~70% task automation
p95 latency: <800ms
Reviewer: Senior staff engineer panel
FastAPILangGraphLangSmithMCPPinecone
Enterprise · weeks 6–11
Async pipeline + eval harness
Build a Celery/Temporal-driven async agent pipeline with a Prefect orchestrator, a golden-dataset eval suite, regression tests on every PR, and a dashboard tracking hallucination + cost guardrails.
TemporalPrefectLangSmithpytest
Real-time · weeks 8–12
RAG service auto-tuner
Stand up a self-tuning RAG service: hybrid retrieval, automatic chunk size + embedding model A/B testing, latency/cost SLOs, and an autonomous agent that picks the best config per workspace.
RAGHybrid SearchVector DBEval
Project · weeks 11–12
Your AI Python agent in a real partner org.
Pick a real partner workflow. Deploy a production Python + LangGraph agent service with MCP tooling, evaluation, and observability — into 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 Python AI agents to production.
MK
Manikanta Kona
Founder, Digital Lync · Python + AI Architect
Python · FastAPI · LangChain · LangGraph · MCP · RAG · Vector DBs
"Python + AI agents in 2026 aren't a chatbot tutorial. They're typed FastAPI services, LangGraph topologies you can debug, hybrid RAG you can evaluate, and MCP tools other agents can call. That's the bar I teach to, every cohort."
15 yrs
PYTHON + AI
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of Python backend and AI platform architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led FastAPI gateways, async agent pipelines, and production LangChain + LangGraph deployments for Fortune-500 banks, telcos, and insurers. Most recently he architected production multi-agent backends with MCP tooling that replaced traditional triage tiers with autonomous case-handling.
His classes get you two things other programs don't give you: a founding architect who's shipped Python + AI agents from inside the Fortune 500, and a curriculum rewritten every quarter — so when hiring managers ask about LangGraph state, MCP server design, or evaluation harnesses, you've already built it. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · AI Backend & Eval Lead
Python · FastAPI · LangGraph · MCP · Vector DBs · Evals · Agent Observability
"Production agent backends earn their keep when the eval harness is louder than the demo. LangGraph you can replay, LangSmith golden datasets, observability that catches hallucinations and cost spikes before users do — that's what I teach."
10 yrs
PYTHON
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the AI backend and evaluations practice. After 8 years building and running production Python services and DevOps pipelines, he stepped into the Chief Technologist seat to wire LangGraph, MCP, and agent observability into the way enterprise teams actually ship — typed FastAPI gateways, async pipelines that survive partial failure, and eval harnesses that catch regressions before deploy.
His agent-backend modules are built from real incident post-mortems, not slide decks. Expect to leave with working LangGraph topologies, LangSmith eval suites, MCP servers, and a vector-DB-backed RAG service you can stake an SLO on. Ten years at Digital Lync, eight of them shipping Python in production — Hyderabad-based, hands-on, and known for the unglamorous parts of agent engineering that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What Python & AI engineering employers say about Digital Lync grads.
Real feedback from engineering leaders at AI-first companies and the firms hiring our Python + AI Agents graduates.
Digital Lync grads ramp 40% faster on Python AI agent backends than typical backend hires. Best Python AI engineering pipeline in India.
Aakash Mehta, Engineering Director, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for Python AI agent backends and eval practices.
Anita Sharma, Senior Manager, Deloitte
The Python + AI Agents programme is comprehensive — FastAPI, LangGraph, MCP, evals. Grads come pre-trained for production AI agent engineering.
Rahul Bhatt, Solutions Lead, Mphasis
Their LangGraph + eval harness track produces PMs who write production-grade Python services on day one. Rare combination of engineering rigor and AI craft.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the agentic backend layer baked into the Python AI track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their LangChain Academy + Pragmatic AI Engineer prep is rigorous, and the shipped project — FastAPI service, MCP server, eval harness — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's Python engineers ship production agent backends twice as fast in the first 90 days. Our internal engineering metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best Python AI engineering pipeline we've sourced from in India. Their projects are real shipped agent services, not toy demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong Python and FastAPI engineering foundation. Their Python AI grads need almost zero ramp time on enterprise agent backend engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our Python AI and watsonx engineering teams. Strong fundamentals, sharp on eval and observability.
Sanjay Verma, Talent Director, IBM
agent backends + LangGraph evals 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 Python AI track delivers engineers who navigate FastAPI, LangGraph, and MCP on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our Python AI engineering practice. Strong on Python, sharp on LangGraph, fluent in agent eval.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync grads who blend FastAPI services with LangGraph + Azure OpenAI evals land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Lync · Institute Certificate
Agent‑Ready Python Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a Python AI agent service —
FastAPI backend, LangGraph topology, and an eval harness — evaluated against the LangChain Academy badges and Pragmatic AI Engineer credential rubrics.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the Python AI engineering community and mapped to LangChain Academy
and Pragmatic AI Engineer credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — FastAPI agent service, MCP server, and eval harness — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: FastAPI services, LangGraph agents, MCP servers, eval harnesses,
observability & 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 Python + AI Agents offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into AI-first SaaS and engineering teams. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your FastAPI agent service, MCP server, eval dashboard, observability dashboard, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the Python services you shipped (agent backends, RAG services, eval harnesses), the partner-org project, 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 and engineering teams — Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, Postman. You leave with recruiter contacts, not a generic "good luck."
Python AI alumni
Hundreds of Python AI engineering careers launched — here are eight.
NV
Naveen Vedala
LangGraph Backend Lead
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
MCP Server Engineer
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Senior AI Backend Engineer
Seattle · United States
Now at · Anthropic
MM
Mujahed Mohammed
RAG Engineer
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
Agent Eval Lead
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
Staff Python Engineer (AI)
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Principal AI Backend Engineer
Hyderabad · India
Now at · Stripe
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Senior AI Backend Engineer 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 Python AI cohorts running on IST and PST. Every online cohort ships
the same shipped project — FastAPI agent service, MCP server, eval harness, observability — 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 Python + AI stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior Python experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA, and self-taught coders. Weeks 1–2 cover the Python fundamentals, FastAPI patterns, and agent design from scratch. What you do need: consistency and 12–15 hours a week.
Will I actually build agents in production, or only do tutorials?
You actually build. Every learner ships a working FastAPI + LangGraph backend wired to OpenAI/Anthropic, an MCP server with auth and rate-limit, and a real eval harness with golden datasets. The project is a deployed agent service inside a partner org — not a tutorial repo.
Which tools and AI models will I use?
Backend: Python, FastAPI, Pydantic, SQLAlchemy, Postgres, Redis, Celery. AI stack: OpenAI, Anthropic, Hugging Face, LangChain, LangGraph, LangSmith, MCP. Vector DBs: Pinecone, Chroma, Weaviate. Ops: Docker, Kubernetes, Terraform, AWS, GitHub Actions.
Will I prep for AIPMM Python AI Engineer and Pragmatic Senior AI Backend Engineer certs?
Yes. The curriculum is mapped to the AIPMM Python AI Engineer track and the Pragmatic Senior AI Backend Engineer credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours building your agent service, and ~5 hours of project work (FastAPI, LangGraph, evals). Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire Python AI engineers?
Yes — a dedicated placement advisor from week 8, not a helpdesk. Python AI hiring partners include Microsoft, Anthropic, OpenAI partners, Hugging Face, LangChain, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, Postman, plus services that staff Python AI agent teams (Deloitte, Accenture, TCS, 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 shipped project — FastAPI agent service, MCP server, eval harness, observability — 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 PYA-020 starts 05 May 2026.
52 seats. 14 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 021.
Call Us
Call Us (Or) Whatsapp Us 
IND: +91
9000329956