SB
Spandana Bala
AI Engineer
Hyderabad · India
Now at · Microsoft
AI Engineer · GenAI · Agentic AI · Coding agents · Enrolling now
AI Engineer · GenAI & Agentic AI
A career-focused, code-first program: build strong Python, FastAPI and LLM engineering foundations, then build production 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 AI Engineer alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is the AI Engineer (GenAI & Agentic AI) program?
An AI engineer builds software on top of frontier models — agents that reason, call tools, retrieve knowledge and act — and makes it reliable, safe and affordable in production. Most AI courses end at a chatbot demo. This program ends only when you have shipped a multi-agent system with MCP tools, RAG grounding, an eval harness with regression tests, guardrails, tracing and a cost budget — deployed behind an API and used by real people.
The complete delivery chain
Twelve links, one owner — end to end.
01FOUNDATIONS
- •Python, typing, async & testing
- •SQL, data handling & Pydantic
- •FastAPI services, Docker & deployment
- •How LLMs work — tokens, context, cost
02BUILD
- •Prompting, structured outputs & tool calling
- •RAG, embeddings & context engineering
- •Agents with Claude Agent SDK, OpenAI Agents SDK & LangGraph
- •MCP servers, clients & agent skills
03SHIP
- •Eval harnesses, datasets & LLM-as-judge
- •Guardrails, red-teaming & safety
- •Tracing, observability & cost control
- •CI/CD, deployment & coding-agent workflows
The AI engineering landscape · 2026
Models became agents. AI engineering became a discipline.
Frontier models & open weightsModel layer
Claude, GPT, Gemini and Grok alongside open-weight Llama, Qwen, DeepSeek and Mistral. Long context, native tool use, structured outputs and reasoning modes — and the cost and latency trade-offs between them.
Agent SDKsBuild layer
Claude Agent SDK, OpenAI Agents SDK and Responses API, LangGraph, Google ADK and Pydantic AI — the frameworks that turn a model into a looping, tool-using agent with state, handoffs and human-in-the-loop.
MCP & agent interoperabilityProtocol layer
The Model Context Protocol became the standard for connecting agents to tools and data; A2A and agent skills extend it across vendors. Salesforce, ServiceNow, GitHub and thousands of servers now speak it.
Coding agentsDeveloper workflow
Claude Code, Codex, Cursor and GitHub Copilot agents write, test and ship code from a spec. AI engineers direct and review them — and build the skills, MCP servers and evals that make them safe.
Evals & observabilityReliability layer
Eval datasets, LLM-as-judge, regression suites and tracing with LangSmith, Langfuse, Braintrust and OpenTelemetry — the discipline that separates demos from production systems.
Safety, guardrails & governanceTrust layer
Prompt-injection defence, output filtering, permissions, audit trails and the EU AI Act, NIST AI RMF and ISO 42001 — what enterprises audit before an agent touches their data.
What this means for your career: AI engineer roles now ask for agent frameworks, MCP, RAG and evals alongside Python and APIs — the differentiator is a deployed agent system with an eval harness, tracing and a cost story, not a notebook of prompts.
Who should join
Built for people moving into AI engineering.
CS / IT graduates & career switchers
Software developers (any language)
Data analysts & data engineers
QA & automation engineers
ML engineers moving to LLM systems
Product & technical founders
Prior experience: none required — Python is taught from scratch. The program builds programming and API foundations before LLM engineering, agents and production delivery.
What you will be able to do
Build the agent — and ship it to production.
Write production PythonTyping, async, testing, Pydantic, SQL and FastAPI services in Docker.
Engineer LLM applicationsPrompting, structured outputs, tool calling, streaming, caching, fine-tuning and cost control.
Build RAG & context pipelinesChunking, embeddings, vector and hybrid search, reranking, memory and context engineering.
Build multi-agent systemsClaude Agent SDK, OpenAI Agents SDK and LangGraph with MCP tools, handoffs and human-in-the-loop.
Evaluate and secureEval harnesses, LLM-as-judge, regression suites, guardrails, red-teaming and tracing.
Ship with coding agentsClaude Code and Codex workflows, agent skills, CI/CD and deployment to cloud.
Course curriculum
Twelve sections. 57 modules. Python → LLMs → RAG → Agents → MCP → Evals → Ship.
01
Fundamentals of IT & AI
How software, the web and AI systems fit together — the vocabulary and mental models everything else builds on.
Operating systems, terminals and the command line
Client-server, HTTP, REST and JSON
Git and GitHub fundamentals
Cloud basics — compute, storage, networking
Frontier models — Claude, GPT, Gemini, Grok — and open weights
From chatbots to agents: tool use, reasoning, computer use
MCP, A2A and agent interoperability
AI engineering as a discipline — build, evaluate, ship
Tokens, embeddings and transformers (intuition level)
Context windows, attention and long context
Pre-training, post-training and reasoning modes
Hallucination, non-determinism and their engineering consequences
VS Code, Python, uv and virtual environments
API keys, secrets and .env hygiene
Claude Code and Codex CLI setup
Your first LLM API call
Safety, bias and privacy risks
EU AI Act, NIST AI RMF and ISO 42001 overview
Data handling and zero-retention options
What enterprises audit before deploying agents
02
Python for AI & Data
Modern Python written the way AI teams write it.
Variables, types, control flow and functions
Lists, dicts, sets and comprehensions
Modules, packages and the standard library
Error handling
Classes, dataclasses and inheritance
Protocols and typing
Decorators, generators and iterators
Functional patterns
Type hints and mypy / pyright
Pydantic models and validation
Settings management
Schemas for LLM structured outputs
asyncio, tasks and gather
Async HTTP with httpx
Rate limiting and retries
Concurrency patterns for LLM calls
pytest, fixtures and mocking
Ruff, formatting and pre-commit
Test-driven development with coding agents
Project structure
03
SQL & Data for AI
Data foundations — because every agent is only as good as the data it reads.
SELECT, WHERE, JOIN, GROUP BY
Subqueries and CTEs
Indexes and query performance
PostgreSQL and SQLite
DataFrames, cleaning and transformation
Working with CSV, JSON and Parquet
Exploratory analysis
Preparing datasets for RAG and evals
Relational vs document vs vector stores
SQLAlchemy and migrations
pgvector and hybrid storage
Data privacy and PII handling
Schema-aware prompting
Text-to-SQL with validation and guardrails
Semantic layers for agents
Evaluating data agents
04
FastAPI & Production Services
Wrap models and agents in services people can actually call.
Routes, request and response models
Dependency injection
Validation and error handling
OpenAPI docs
Server-sent events for token streaming
Background tasks and queues
WebSockets for agent UIs
Long-running agent runs
API keys, OAuth 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
Streamlit and Gradio for prototypes
Next.js chat UIs (overview)
Generative UI patterns
Human-in-the-loop interfaces
05
LLM Engineering
Working with models as an engineer — reliably, cheaply and at scale.
Messages API and Responses API
System prompts, roles and multi-turn
Model selection — quality, cost, latency
Running open-weight models with Ollama and vLLM
Instruction design, examples and reasoning prompts
Context engineering — what goes in the window and why
Prompt caching and extended thinking
Prompt versioning and management
JSON schema and Pydantic structured outputs
Function / 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
Supervised fine-tuning with open weights and provider APIs
Distillation and small models
Evaluating fine-tuned models
Token accounting and budgets
Caching, batching and routing
Latency optimisation and streaming
Cost dashboards
06
RAG & Knowledge Systems
Ground models in your data — and prove the retrieval works.
Embedding models and dimensions
Vector databases — pgvector, Qdrant, Pinecone, Chroma
Similarity search and filtering
Indexing at scale
Parsing PDFs, HTML and office docs
Chunking strategies and metadata
Document intelligence and OCR
Incremental ingestion pipelines
Hybrid search and reranking
Query rewriting and HyDE
Agentic and graph RAG
Citations and grounding verification
Short-term vs long-term memory
Memory stores and summarisation
User and session memory patterns
Privacy in memory systems
Retrieval metrics — recall, precision, MRR
RAG eval frameworks — RAGAS and custom
Building golden datasets
Iterating on retrieval quality
07
Agentic AI Foundations
What an agent is, how it loops, and how to design one that finishes the job.
The agent loop — perceive, reason, act
ReAct, plan-and-execute and reflection
Single vs multi-agent designs
When not to build an agent
Agent SDK fundamentals and the agent loop
Built-in tools, subagents and hooks
Agent skills and memory
Building a research and coding agent
Agents, tools, handoffs and guardrails
Responses API and built-in tools
Sessions and tracing
Building a customer support agent
Graphs, nodes, edges and state
Persistence, checkpoints and time travel
Human-in-the-loop interrupts
LangGraph Platform deployment
Google Agent Development Kit
Pydantic AI and type-safe agents
CrewAI and role-based teams
Choosing a framework
08
MCP & Tool Engineering
Connect agents to the world — safely and reusably.
MCP architecture — hosts, clients, servers
Tools, resources and prompts
Transports — stdio and streamable HTTP
The MCP ecosystem and registries
Python and TypeScript MCP SDKs
Designing tool contracts agents understand
Auth, scopes and permissions
Testing and publishing servers
Idempotent, bulk-safe tool design
Error contracts and retries
Confirmation and approval patterns
Tool evaluation
Connecting agents to enterprise systems through MCP
Salesforce Headless Toolkit and ServiceNow MCP server
GitHub and Slack integrations
Governance across MCP boundaries
A2A protocol and agent cards
Agent skills and shared standards
Multi-vendor agent orchestration
Trust boundaries between agents
09
Multi-Agent Systems & Orchestration
Coordinate agents that plan, delegate and recover.
Supervisor, swarm and hierarchical patterns
Handoffs and shared state
Parallelism and map-reduce agents
Failure handling and fallbacks
Planning strategies and task decomposition
Reflection and critique loops
Verification agents
Stopping criteria
Interrupts and approval gates
Escalation to humans
Feedback capture
UX for supervised agents
Durable execution and checkpoints
Queues, schedulers and workers
Resumability and idempotency
Monitoring background agents
Support, research, sales and operations agents
Data and analytics agents
Workflow automation agents
Measuring business value
10
Evals, Guardrails & Observability
The discipline that separates demos from production.
Why evals matter and what to measure
Golden datasets and rubric design
Offline vs online evaluation
Evals in CI
LLM-as-judge design and calibration
Trajectory and tool-use evaluation
Task-completion evals for agents
Braintrust, LangSmith, Langfuse and promptfoo
Input and output guardrails
Prompt injection and jailbreak defence
PII detection and content filtering
Permission boundaries for tools
Attack taxonomies
Automated red-teaming
Data exfiltration and tool abuse tests
Documenting findings and fixes
OpenTelemetry for LLM apps
Traces, spans and token accounting
Dashboards and alerts
Debugging agent failures
11
Coding Agents & AI-Native Development
Work with the agents that write software — and build the ones your team will use.
Claude Code fundamentals and workflows
CLAUDE.md, skills and subagents
MCP servers in Claude Code
Spec-driven development
Codex CLI and cloud agents
Cursor agent mode
GitHub Copilot coding agent and PR workflows
Choosing tools per task
Repository understanding and file tools
Plan, edit, run, test loop
Sandboxing and safety
Evaluating coding agents with SWE-bench-style tasks
Spec and test-first workflows with agents
Code review of AI-generated code
Team conventions and guardrails
Productivity measurement
12
Capstone, Portfolio & Career
One production agent system, evaluated and deployed, plus a portfolio employers can verify.
Multi-agent system with Claude Agent SDK or LangGraph and MCP tools
RAG grounding, memory and FastAPI deployment
Eval harness with regression tests, guardrails and tracing
Cost budget, documentation and a public verification URL
Build and evaluate a coding agent on a real repository
Skills, MCP servers and CI integration
Safety and sandboxing
Demo and write-up
GitHub portfolio with READMEs and demos
Resume rewrite around shipped AI systems
AI engineer interview practice — system design and live coding
Warm introductions to hiring partners
Tools you'll master
32+ GenAI & agentic 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
Production agentic system on LangGraph + MCP + A2A
Build an end-to-end multi-agent platform — supervisor + specialist nodes coordinating over A2A, an MCP server fleet exposing your agents as tools, a hybrid RAG layer, and a full eval + safety harness.
01Multi-agent topology — supervisor + specialist nodes communicating over A2A, with retries, fallback models, replay-capable state, and human-in-the-loop checkpoints.
02MCP server fleet that exposes your agents as tools to Claude/ChatGPT desktop, plus OpenAPI tools for arbitrary REST APIs — auth, scopes, rate-limit, audit logging.
03Production RAG layer with hybrid search across Postgres + Pinecone, contextual chunking, semantic caching, and a LangSmith golden-dataset eval suite.
04Eval + safety harness — DSPy optimization, Guardrails AI / NeMo Guardrails for safety, Arize for drift monitoring, hallucination + cost guardrails on a public dashboard.
Outcome: ~80% task automation
p95 latency: <1s
Reviewer: AI Engineering panel
LangGraphA2AMCPDSPyPinecone
Enterprise
Multi-agent A2A workshop
Build a 5-agent system that negotiates work via the A2A protocol — a supervisor, a researcher, a coder, a reviewer, and a deployer. Each agent runs as its own service with auth, telemetry, and replay.
A2AMulti-agentLangGraphTelemetry
Real-time
DSPy-optimized RAG service
Build a self-tuning RAG service that uses DSPy to automatically optimize prompts and retrieval strategy against a golden dataset, with Arize-tracked drift monitoring.
DSPyRAGArizeLangSmith
Project
Your AI agent system in a controlled project environment.
Pick a real partner workflow. Deploy a production GenAI + agentic system — multi-agent topology, MCP-served tools, A2A coordination, production evals — 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 agentic AI to production.
MK
Manikanta Kona
Founder, Edify Nuva · AI Engineering Architect
GenAI · Agentic AI · LangGraph · MCP · A2A · Production RAG
"Production GenAI is where AI engineers earn their keep — LangGraph orchestrating
multi-agent topologies, MCP serving your tools to any client, A2A coordinating across services, and an eval
harness that catches regressions before users do. That's the bar I teach to, every class."
15 yrs
AI ENGINEERING
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Edify Nuva and brings 15 years of platform architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led production ML and GenAI rollouts for Fortune-500 banks, telcos, and insurers. Most recently he architected production LangGraph + MCP + A2A systems that replaced traditional case-handling tiers with autonomous multi-agent flows, with full eval and observability harnesses behind them.
His classes get you two things other programs don't give you: a founding architect who's shipped agentic AI from inside the Fortune 500, and a curriculum rewritten every quarter — so when hiring managers ask about MCP server fleets, A2A negotiation, DSPy optimization, or LangSmith eval suites, you've already built it. Holds LangChain Academy badges and the AWS Solutions Architect — ML Specialty; M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Edify Nuva · Agent Platform & Eval Lead
LangGraph · MCP · A2A · DSPy · Vector DBs · Evals · Agent Observability
"Shipping a multi-agent system to production is the easy part — keeping it healthy
is the work. MCP fleets that don't drift, A2A handshakes that recover from partial failures, golden
datasets that catch regressions before users do. That's what I teach."
10 yrs
AI ENGINEERING
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Edify Nuva, where he leads the Agent Platform and evaluation practice. After 8 years shipping production ML and DevOps pipelines, he stepped into the Chief Technologist seat to wire LangGraph, MCP fleets, and A2A into the way real engineering teams actually run agents — replay-able state, golden-dataset evals, drift monitoring, and cost guardrails that keep multi-agent systems quiet on purpose.
His agent and eval modules are built from real production post-mortems, not slide decks. Expect to leave with working MCP servers, an A2A-coordinated multi-agent topology, a DSPy-optimized RAG service, and an Arize + LangSmith observability stack you can stake an SLA on. Holds the Pragmatic AI Engineer track credential and Azure AI Engineer Associate; ten years at Edify Nuva, hands-on, and known for the unglamorous parts of agentic AI that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What AI engineering employers say about Edify Nuva grads.
Real feedback from engineering leaders at AI labs and the firms hiring our AI Engineer · GenAI & Agentic graduates.
Edify Nuva grads ramp 40% faster on GenAI agent rollouts than typical AI engineering hires. Best GenAI 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 multi-agent systems and eval practices.
Anita Sharma, Senior Manager, Deloitte
The AI Engineer · GenAI & Agentic programme is comprehensive — LangGraph, MCP, A2A, evals. Grads come pre-trained for production agentic AI engineering.
Rahul Bhatt, Solutions Lead, Mphasis
Their LangGraph + DSPy + eval track produces PMs who ship production multi-agent systems on day one. Rare combination of engineering rigor and AI craft.
Deepak Pillai, Senior Architect, TCS
What sets Edify Nuva apart is the agentic platform layer baked into the AI engineer 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 — multi-agent system, MCP fleet, eval harness — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Edify Nuva's AI engineers ship production multi-agent systems twice as fast in the first 90 days. Our internal engineering metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best GenAI engineering pipeline we've sourced from in India. Their projects are real shipped agent systems, not toy demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong GenAI and agentic engineering foundation. Their AI Engineer grads need almost zero ramp time on enterprise agent platform engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Edify Nuva alumni across our GenAI and watsonx engineering teams. Strong fundamentals, sharp on eval and observability.
Sanjay Verma, Talent Director, IBM
multi-agent systems + 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 AI Engineer track delivers engineers who navigate LangGraph, MCP, and A2A on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Edify Nuva graduates for our GenAI engineering practice. Strong on LangGraph, sharp on MCP/A2A, fluent in agent eval.
Geetha Pillai, Talent Acquisition Lead, Cyient
Edify Nuva grads who blend multi-agent systems with 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 AI Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a multi-agent system —
LangGraph topology, MCP server fleet, A2A coordination, and an eval harness — evaluated against the LangChain Academy badges, AWS ML Specialty, and Pragmatic AI Engineer credential rubrics.
Manikanta Kona
CEO · Edify Nuva
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the 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 — multi-agent system, MCP fleet, A2A coordination, 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: LangGraph topologies, MCP servers, A2A coordination, eval harnesses,
drift monitoring & 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.
AI Engineer
Build LLM applications and agents from prototype to production.
Agentic AI Engineer
Design and ship multi-agent systems with MCP tools and evals.
GenAI Application Developer
Build RAG, chat and copilot features into products.
LLM / RAG Engineer
Own retrieval pipelines, embeddings and grounding quality.
AI Platform Engineer
Build the eval, observability and deployment platform for AI teams.
Forward Deployed AI Engineer
Embed with customers to ship agents into their systems.
AI Solutions Engineer
Design, demo and integrate AI solutions for enterprise customers.
MCP / Integration Engineer
Build MCP servers and tools connecting agents to enterprise data.
AI Quality & Evaluation Engineer
Own eval harnesses, red-teaming and guardrails.
AI Architect (career path)
Grow toward designing enterprise agent platforms.
What employers should see in your portfolio: that you can take an idea to a production agent — Python and FastAPI service, RAG grounding, agents built on the Claude Agent SDK or LangGraph with MCP tools, an eval harness with regression tests, guardrails and tracing, deployed with a cost budget and used by real people.
04Job placement support
Your first AI Engineer offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into AI labs and AI-first product orgs. 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 multi-agent system, MCP fleet, A2A coordination, eval 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 AI systems you shipped (multi-agent topologies, MCP fleets, eval harnesses), the partner-org project, and the business outcome. Reviewed by AI engineers who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into AI labs and AI-first product orgs — Microsoft, Anthropic, OpenAI partners, Hugging Face, LangChain, Cohere, Mistral, Databricks, Snowflake, Scale AI, Stripe, Razorpay, Freshworks, Zoho, plus services that staff GenAI teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
AI Engineer alumni
Hundreds of AI engineering careers launched — here are eight.
NV
Naveen Vedala
Senior AI Engineer (Agent Platforms)
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Staff GenAI Engineer
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Principal Engineer (Multi-Agent Systems)
Seattle · United States
Now at · Anthropic
MM
Mujahed Mohammed
LangGraph Backend Lead
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
MCP Server Engineer
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
RAG Engineer
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
AI Evaluation Lead
Hyderabad · India
Now at · Cohere
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Principal Engineer (Multi-Agent Systems) 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 AI Engineer classes running on IST and PST. Every online class ships
the same shipped project — multi-agent system, MCP fleet, A2A coordination, eval harness — 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 GenAI / agentic 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 ML 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 GenAI fundamentals, LangGraph patterns, and agent design from scratch. What you do need is consistency and regular practice.
Will I actually ship production agents, or only build toy demos?
You actually ship. Every learner deploys a working multi-agent system on LangGraph with MCP-served tools, A2A coordination between agents, and a real eval harness with golden datasets, drift monitoring, and cost guardrails. The project runs in a partner org — not a notebook.
Which models, frameworks, and protocols will I use?
Models: OpenAI, Anthropic, Gemini, Hugging Face, vLLM, Ollama. Frameworks: LangChain, LangGraph, LangSmith, LlamaIndex, DSPy. Protocols: MCP, A2A. Vector DBs: Pinecone, Chroma, Weaviate, Qdrant, Milvus. Safety & obs: Guardrails, NeMo Guardrails, Arize, MLflow, Weights & Biases.
Will I prep for AIPMM AI Engineer and Pragmatic Principal Engineer (Multi-Agent Systems) certs?
Yes. The curriculum is mapped to the AIPMM AI Engineer track and the Pragmatic Principal Engineer (Multi-Agent Systems) 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 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 — multi-agent system, MCP fleet, A2A coordination, eval harness — 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.
