SB
Spandana Bala
Python AI Engineer
Hyderabad · India
Now at · Microsoft
Python · FastAPI · Automation · AI agents · MCP · Enrolling now
Python & AI Agents
A career-focused, code-first program: build strong Python, SQL, FastAPI and automation foundations, then build AI agents with the Claude Agent SDK, OpenAI Agents SDK and LangGraph — wired to tools through MCP, grounded with RAG, and shipped with evals, guardrails and observability.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Edify Nuva centres
Where our Python & AI alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is the Python & AI Agents program?
A Python engineer writes the code behind automation, back-end services and AI — and now the agents that reason, call tools and act on real systems. Most Python courses end at scripts and a Flask app; most AI courses end at a chatbot. This program ends only when you have shipped a FastAPI service with agents built on the Claude Agent SDK or LangGraph, MCP tools you wrote, RAG grounding, an eval harness, guardrails and tracing — deployed and used by real people.
The complete delivery chain
Twelve links, one owner — end to end.
01FOUNDATIONS
- •Python syntax, OOP, typing & async
- •SQL, SQLAlchemy & PostgreSQL
- •pytest, Ruff, uv & packaging
- •Automation, CLIs & scraping
02BUILD
- •FastAPI services, auth & Docker
- •LLM engineering, structured outputs & tools
- •RAG with pgvector & embeddings
- •Agents with Claude Agent SDK, OpenAI Agents SDK & LangGraph
03SHIP
- •MCP servers with FastMCP
- •Evals, guardrails & tracing
- •CI/CD & cloud deployment
- •Coding-agent workflows with Claude Code & Cursor
Python in 2026
Python became the language of AI. Python engineers build the agents.
Python is the AI defaultLanguage layer
Every major model SDK, agent framework and eval tool ships Python first — Anthropic, OpenAI, Google, LangGraph, Pydantic AI. Python 3.12+, uv and typed code are the modern baseline.
Agent SDKsBuild layer
Claude Agent SDK, OpenAI Agents SDK, LangGraph, Pydantic AI and Google ADK turn a model into a looping, tool-using agent with state, handoffs and human-in-the-loop — all in Python.
MCP & FastMCPProtocol layer
The Model Context Protocol became the standard for connecting agents to tools; FastMCP makes a Python function an MCP tool in a few lines. Scripts you already write become agent capabilities.
FastAPI & asyncService layer
FastAPI, Pydantic and asyncio are how agents are served — streaming responses, background runs and typed contracts. Django and Flask remain in enterprise codebases.
Coding agentsDeveloper workflow
Claude Code, Cursor and Codex write and test much of the code. Python engineers direct and review — and build the skills, MCP servers and evals that keep generated code honest.
Evals, guardrails & safetyReliability layer
Eval datasets, LLM-as-judge, regression suites, tracing and prompt-injection defence — the discipline that separates demos from production systems.
What this means for your career: Python roles now ask for agent frameworks, MCP, RAG and evals alongside FastAPI and SQL — the differentiator is a deployed agent service with MCP tools, an eval harness and tracing, not a folder of scripts.
Who should join
Built for people starting in Python — and going straight to agents.
Complete beginners & graduates
Career switchers into software or AI
Testers, support & ops engineers moving to development
Analysts & scientists who want to automate
Developers in other languages adding Python & AI
Founders & product builders
Prior experience: none required — Python is taught from scratch. The program builds programming, SQL and API foundations before LLM engineering, agents and production delivery.
What you will be able to do
Write real Python — and ship agents that work.
Write production PythonModern syntax, OOP, typing, async, pytest, Ruff, uv and packaging.
Build APIs and automationsFastAPI services with auth and PostgreSQL; CLIs, scraping and workflow automation.
Engineer LLM featuresPrompting, structured outputs, tool calling, multimodal, cost control and caching.
Build RAG pipelinesEmbeddings, vector search, chunking, reranking, memory and retrieval evals.
Build agents and MCP toolsClaude Agent SDK, OpenAI Agents SDK, LangGraph and Pydantic AI; MCP servers with FastMCP.
Ship with confidenceEvals, guardrails, tracing, CI/CD, Docker and coding-agent workflows.
Course curriculum
Twelve sections. 60 modules. Python → SQL → FastAPI → Automation → LLMs → RAG → Agents → MCP → Ship.
01
Fundamentals of IT & AI
How software and AI systems fit together — and where a Python engineer works in the 2026 stack.
Client-server, HTTP, REST and JSON
Operating systems, terminals and Git
Databases and cloud basics
How Python fits in backend and AI systems
Frontier models — Claude, GPT, Gemini — and open weights
From chatbots to agents: tool use and reasoning
MCP and agent interoperability
Python as the language of AI tooling
Tokens, context windows and cost
Non-determinism and its engineering consequences
Structured outputs and tool calling at a glance
What LLMs can and cannot do
Python 3.12+, uv and VS Code
Git and GitHub workflows
Claude Code, Cursor and Copilot for Python
Reviewing AI-generated code
Privacy and PII
Prompt injection and AI-specific risks
EU AI Act and NIST AI RMF overview
Secure-by-default habits
02
Python Fundamentals
The language from first principles — written the modern way.
Variables, numbers, strings and booleans
Conditionals and loops
Functions and scope
Modules and the standard library
Lists, tuples, sets and dictionaries
Slicing and iteration
Comprehensions and generators
Sorting and built-ins
Reading and writing files
JSON and CSV
Exceptions and error handling
Logging
Classes, attributes and methods
Inheritance, protocols and dunder methods
Dataclasses and enums
Design for readability
Lambdas, map, filter and functools
Iterators and generators
Decorators and context managers
Functional patterns
uv, virtual environments and pyproject
Ruff formatting and linting
Type hints and pyright / mypy
Project structure and packaging
03
Intermediate & Advanced Python
The depth interviewers and production codebases expect.
Type hints, generics and Protocols
Pydantic models and validation
Settings management
Schemas for LLM structured outputs
asyncio, tasks and gather
Async HTTP with httpx
Threads, processes and the GIL
Concurrency patterns for LLM calls
Tests, fixtures and parametrisation
Mocking and patching
Coverage and TDD
Testing with coding agents
Debugging tools
Profiling and optimisation
Caching and memoisation
Common performance pitfalls
Memory model and object lifecycle
Common design patterns in Python
Clean architecture for services
Refactoring with AI assistance
04
SQL & Data Access
Relational data — the foundation of every backend and most AI features.
SELECT, JOIN, GROUP BY and set operations
Subqueries and CTEs
Data types and constraints
PostgreSQL
Window functions
Indexes and query plans
Transactions and isolation
Query tuning
SQLAlchemy 2.0 ORM and Core
Alembic migrations
Repository patterns
pgvector for embeddings
DataFrames, cleaning and joins
Polars and DuckDB for larger data
Parquet and Arrow
Preparing data for RAG and evals
05
FastAPI & Backend Engineering
Wrap logic, models and agents in services people can call.
Routes, request and response models
Dependency injection
Validation and error handling
OpenAPI docs
Server-sent events for token streaming
Background tasks and queues — Celery, arq
WebSockets for agent UIs
Long-running agent runs
API keys, OAuth2 and JWT
CORS and input validation
Rate limiting and abuse protection
Secrets management
Dockerfiles and compose
Deploying to Railway, Fly.io, AWS or GCP
Environment configuration
Health checks and logging
Django and Django REST Framework
Flask
Choosing a framework
Migrating between frameworks
Streamlit and Gradio for prototypes
HTMX and Jinja templates
Chat UIs for agents (overview)
Human-in-the-loop interfaces
06
Automation & Scripting with Python
The everyday superpower — automate anything, then hand it to an agent.
argparse and Typer
Rich terminal output
Packaging CLIs
Scheduling scripts
requests, httpx and BeautifulSoup
Playwright for browser automation
Working with REST and GraphQL APIs
Rate limits and ethics
Excel, PDF and document automation
Email and Slack automation
Cron, GitHub Actions and n8n
Error handling and notifications
Turning scripts into callable tools
Input and output contracts
Safety and idempotency
Exposing tools through MCP
07
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 and vLLM
Instruction design and examples
Context engineering — what goes in the window
Prompt caching and extended thinking
Prompt versioning
JSON schema and 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 evaluation
When to fine-tune vs prompt vs RAG
LoRA and provider fine-tuning
Token accounting and budgets
Caching, batching and routing
08
RAG & Knowledge Systems
Ground models in your 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 and graph RAG
Citations and grounding verification
Short-term vs long-term memory
Memory stores and summarisation
User and session memory
Privacy in memory systems
Retrieval metrics
RAGAS and custom eval frameworks
Golden datasets
Iterating on quality
09
Agentic AI — Frameworks & Patterns
Build agents that finish the job, with the Python frameworks 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 operations agent
Deployment patterns
Agents, tools, handoffs and guardrails
Responses API and built-in tools
Sessions and tracing
Building a support agent
Graphs, nodes, edges and state
Persistence and checkpoints
Human-in-the-loop interrupts
LangGraph Platform
Pydantic AI and type-safe agents
Google Agent Development Kit
CrewAI and role-based teams
Choosing a framework
Supervisor and swarm patterns
Handoffs and shared state
Approval gates and escalation
Failure handling and fallbacks
10
MCP & Tool Engineering
Connect agents to tools and data — safely and reusably.
Hosts, clients, servers, tools, resources and prompts
Transports and auth
The MCP ecosystem
Security model
The Python MCP SDK and FastMCP
Exposing APIs, databases and scripts as tools
Tool contracts agents understand
Testing and publishing
Idempotent, safe tool design
Error contracts and retries
Confirmation and approval patterns
Tool evaluation
Salesforce, ServiceNow, GitHub and Slack MCP servers
A2A and agent skills
Connecting Claude and Cursor to your tools
Permissions across boundaries
11
Evals, Guardrails, Observability & Deployment
The discipline that separates demos from production — and how to ship.
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
GitHub Actions pipelines
Docker and cloud deployment
Environments and secrets
Monitoring and incident response
Claude Code workflows, CLAUDE.md and skills
Cursor and Codex
Spec-first and test-first with agents
Code review of AI-generated code
12
Capstone, Portfolio & Career
A production Python agent system with MCP tools, evals and real users — verifiable by employers.
FastAPI service with auth, PostgreSQL and pgvector
Agents with Claude Agent SDK or LangGraph, MCP tools and RAG
Eval harness, guardrails and tracing
Deployment with a public verification URL
Automate a real workflow with Python
Expose it as an MCP server
Use it from Claude and Cursor
Demo and write-up
PCEP and PCAP Python certifications
Cloud practitioner or developer associate (optional)
Practice exams
Study plan
GitHub portfolio with READMEs and demos
Resume rewrite around shipped systems
Python interview practice — DSA, system design, live coding
Warm introductions to hiring partners
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 portfolio projects and a partner capstone, each threaded through the entire curriculum — Python, FastAPI, agents, MCP and evals all land in real deliverables.
Hero project
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
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
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
Your AI Python agent in a controlled project environment.
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 deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Production-style capstoneCareer support included
Your instructor
Taught by engineers who shipped Python AI agents to production.
MK
Manikanta Kona
Founder, Edify Nuva · 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 class."
15 yrs
PYTHON + AI
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Edify Nuva 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, Edify Nuva · 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 Edify Nuva, 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 Edify Nuva, 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 Edify Nuva grads.
Real feedback from engineering leaders at AI-first companies and the firms hiring our Python & AI Agents graduates.
Edify Nuva 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+ Edify Nuva 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 Edify Nuva 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
Edify Nuva'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+ Edify Nuva 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. Edify Nuva 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+ Edify Nuva graduates for our Python AI engineering practice. Strong on Python, sharp on LangGraph, fluent in agent eval.
Geetha Pillai, Talent Acquisition Lead, Cyient
Edify Nuva 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.
Edify Nuva · 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 · Edify Nuva
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.
Job roles
Roles this program prepares you for.
Python Developer
Back-end services, automation and integrations in Python.
AI Engineer (Python)
LLM features, RAG and agents shipped as services.
Agentic AI Engineer
Multi-agent systems with MCP tools and evals.
Backend Engineer (FastAPI / Django)
APIs, data and agent services behind products.
Automation Engineer
Scripts, workflows and agent tools that remove manual work.
MCP / Integration Engineer
MCP servers and tool integrations for agents.
AI Quality & Evaluation Engineer
Eval harnesses, red-teaming and guardrails.
Data / Analytics Engineer (Python)
Pipelines and data tooling with pandas, Polars and SQL.
Solutions Engineer (AI)
Prototype and integrate AI features for customers.
Senior Python / AI Engineer (career path)
Grow toward leading AI engineering teams.
What employers should see in your portfolio: that you can take an idea to a shipped agent service — clean, typed Python; a FastAPI back end on PostgreSQL and pgvector; agents built on the Claude Agent SDK or LangGraph with MCP tools and RAG; evals, guardrails and tracing; deployed and used by real people.
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 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 Python AI classes running on IST and PST. Every online class 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
Admissions
ENROLLING NOW
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. The opening modules cover the Python fundamentals, FastAPI patterns, and agent design from scratch. What you do need is consistency and regular practice.
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.
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 Python 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 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 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.
