SB
Spandana Bala
AI Engineer
Hyderabad · India
Now at · Microsoft
Class 014 · AI ENGINEER · GENAI + AGENTIC AI
AI Engineer
+ Coding
Agent
The focused AI Engineer programme for 2026. Master the Python + FastAPI + PostgreSQL stack, deep GenAI with frontier models, and Agentic AI with LangGraph, Claude Agent SDK, and MCP — culminating in a deployed AI Coding Agent project.
3mo
duration
30+
modules
4.7/5
class rating
100k+
enrolled
Where our AI Engineer alumni work
Microsoft
Amazon
SalesforceAI Engineer
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every AI Engineer grad walks away with.
01
Agent-Ready skills
Production Python + PostgreSQL + FastAPI fused with frontier model mastery, RAG, and the 2026 agentic stack — LangGraph, Claude Agent SDK, CrewAI, MCP.
02
A shipped Coding Agent
An autonomous Coding Agent project — LangGraph state machine, Claude Agent SDK, CrewAI multi-agent crew, MCP servers, deployed with LangSmith observability.
03
Verifiable credential
2026 Agent-Ready rubric mapped to PCEP/PCAP, AI-102, AWS ML Specialty, GCP ML Engineer — graded 1–5 with a public verification URL.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, AI-Engineer-tuned resume rebuild, and warm intros into our 1,000+ hiring partners staffing AI, GenAI, LLM, and Agentic AI Engineer roles.
3 MONTHS · FOUR PHASES · ONE CODING AGENT
From “writes a Python script” to ships AI Coding agents. .
Weeks 1–4 · Foundations
IT/AI Fundamentals + Python for AI
- Application lifecycle, Agile/Scrum, cloud computing models
- Introduction to AI, ML, Generative AI, Agentic AI
- Python fundamentals, data structures, and functional programming
- Advanced Python — decorators, generators, async/await, OOP
YOU SHIPA production-quality Python codebase with OOP, async, type hints, and proper packaging — the fluency that powers every later phase.
Weeks 5–10 · Data Layer + Modern APIs
PostgreSQL + Modern Python FastAPI
- PostgreSQL foundations, advanced SQL, CTEs, and window functions
- PL/pgSQL stored procedures, triggers, and query optimisation
- FastAPI with Pydantic validation, SQLAlchemy ORM, Alembic migrations
- JWT authentication, OAuth2 flows, and role-based access control
YOU SHIPA PostgreSQL analytical database plus a production FastAPI service with Pydantic, JWT auth, SQLAlchemy, and async endpoints.
Weeks 11–18 · GenAI Engineering Depth
Frontier Models + Prompt Engineering + RAG
- Transformer architecture, tokenisation, and frontier model fundamentals
- Mastering GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and open-source models
- Prompt engineering, context engineering, and multimodal AI
- RAG pipelines with ChromaDB, Pinecone, Qdrant, hybrid search and re-ranking
YOU SHIPA GenAI engineering portfolio — 30+ prompt library, multimodal AI app,
production RAG service with hybrid search and re-ranking deployed via FastAPI.
Weeks 19–24 · Agentic AI + Coding Agent Project
Master the 2026 Agentic AI stack — and ship the named AI Coding Agent project that closes every 2026 AI Engineer interview.
2026 Agent Framework Stack — LangGraph 1.0 (#1 production default), Claude Agent SDK (#2 with deepest MCP integration), CrewAI (#3 multi-agent crews), Pydantic AI (type-safe). Core Agent Patterns — ReAct, Plan-and-Execute, Reflection loops, multi-agent collaboration, human-in-the-loop checkpoints. 🆕 Model Context Protocol (MCP) — open standard from Anthropic, now Linux Foundation-stewarded with 200+ server implementations and 97M+ monthly SDK downloads. Build MCP servers exposing code repositories, test suites, execution environments. A2A Protocol for agent-to-agent communication. 🧪 Final Project — The AI Coding Agent — a deployed multi-agent system generating Python code from natural language, running experiments, debugging failures, proposing refactors. Frontend with Streamlit or React, backend with FastAPI, LangSmith observability, public verification URL.
Partner orgs
(2026)54
Projects
deployed310+
→ Placement
offers89%
Course curriculum
Seven sections. 65+ modules. The AI-native AI Engineer stack.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock every AI engineer needs —
application lifecycle, Agile/Scrum, computing infrastructure, and AI/ML/Generative/Agentic AI
fundamentals.
Application fundamentals — types, web architecture
Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python,
Java, Node.js)
Database Systems — SQL (PostgreSQL, MySQL) and NoSQL (MongoDB)
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
AI Engineers sit at the seam of code, data, and AI — knowing how applications
work makes you a better AI system architect
Methodology Evolution — Waterfall vs Agile
Scrum Roles, Events, Artifacts, User Stories
Estimating with story points
Backlog management with Azure Boards, JIRA
CPU vs GPU — when each matters for AI workloads
Cloud Service Models — IaaS, PaaS, SaaS
Public, private, hybrid cloud
GPU economics for AI engineering — when to use cloud GPU, when to call APIs
AI is reshaping software engineering — from AI-generated code to autonomous
agents to production AI services
AI Fundamentals — what AI is, how AI systems work
Machine Learning — algorithms that improve through experience
Deep Learning — neural networks for complex pattern recognition
Generative AI — systems that create text, images, code based on learned patterns
Large Language Models — process and generate human-like text
Image Generation — AI models creating original images from text
Agentic AI — Plan / Reason / Act / Learn loops, tool use
AI in production — chatbots, code assistants, content generation
AI agents in modern workflows
Industry applications — fintech, healthcare, retail, education
Domain depth multiplies your AI Engineer salary
02
Python for AI & Data
The dominant language for AI engineering. Ten modules from environment setup
through advanced OOP — the language fluency that powers every AI engineering job.
Python interpreter installation for Windows and Mac
Visual Studio Code IDE configuration for AI engineering
Python's 35 essential keywords
Variables and memory management
Data types, operators, type conversion
Control flow — if/elif/else, while, for, match-case, break/continue/pass
String fundamentals — indexing, slicing, concatenation
f-strings and .format() for AI prompt construction
String methods — case, search, trimming, replacement
.split() and .join() for text data preprocessing
Critical for parsing LLM responses and constructing prompts
Lists — creation, indexing, slicing, modification
List comprehensions for elegant data transformation
Sorting, reversing, copying patterns
Tuples — immutable sequences with packing and unpacking
Performance advantages over lists
Use cases for immutability in AI applications
Dictionaries — creation, access, operations
Dictionary comprehensions
Nested dictionaries for structured data
Essential for representing LLM API requests/responses and agent state
Sets and the UUU properties (Unique, Unordered, Unindexed)
Mathematical operations — union, intersection, difference
Applications in deduplication and set algebra
Collections module — namedtuple, Counter, defaultdict, deque
Iteration protocol enabling custom iterators
Generators using yield for memory-efficient streaming — essential for processing
large LLM responses
Generator expressions and pipelines
Lambda functions for anonymous functions
Higher-order functions — map(), filter(), reduce()
Function definition, parameters, return values
Default arguments, *args, **kwargs
Variable scope (LEGB rule)
First-class functions and higher-order patterns
Recursion and recursive design patterns
Type hints (Python 3.5+) — essential for self-documenting AI code
Documenting functions with docstrings (PEP 257)
Built-in modules, user-defined modules, packages
pip for package management
requirements.txt for reproducible builds
Virtual environments for isolated AI projects
Reproducibility is critical when AI dependencies change weekly
CRUD operations with open()
File modes and pathlib
Python's csv module — reader, writer, DictReader, DictWriter
JSON operations — dump(), dumps(), load(), loads()
JSON is the language of AI APIs — master it cold
Exception Handling — robust error handling for unreliable LLM APIs, retry
patterns
Decorators — for logging API calls, timing, caching, rate limiting AI calls
Generators deep dive — memory efficiency for streaming LLM responses
Context Managers — proper resource management for DB connections, file handles,
async clients
Four patterns that separate scripting Python from production AI engineering
Classes & Objects, Methods, Special Methods
Instance vs class variables
Encapsulation — access modifiers control data visibility
Inheritance — single, multi-level, and multiple inheritance with super() and
method overriding
Abstraction — abstract classes and methods (from abc module)
Polymorphism — method overriding and duck typing
Pydantic BaseModel, LangChain BaseModel — OOP underpins every modern Python AI
library
03
SQL for AI & Data
The data backbone of AI applications. Five modules covering PostgreSQL from
foundations through programming with PL/pgSQL — the data layer that powers your AI services and your
pgvector RAG implementations.
Databases, DBMS, RDBMS — concepts and terminology
ACID properties — Atomicity, Consistency, Isolation, Durability
PostgreSQL setup, psql, pgAdmin 4, DBeaver
Data types — numeric, character, date/time, boolean, JSON, arrays
Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT
SELECT statements and column projection
WHERE clauses with operators and conditions
Built-in functions — string, numeric, date, conditional
Aggregates — COUNT, SUM, AVG, MIN, MAX
GROUP BY and HAVING
Window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
JOIN operations — INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF
Subqueries — scalar, row, table subqueries
CTEs (Common Table Expressions) — readable analytical queries
Recursive CTEs for hierarchical data
Set operators — UNION, UNION ALL, INTERSECT, EXCEPT
DML — INSERT, UPDATE, DELETE patterns
Transactions — BEGIN, COMMIT, ROLLBACK
ALTER TABLE for schema evolution
Indexes — B-tree, Hash, GiST, GIN
Views — virtual tables, materialized views
Stored functions with CREATE FUNCTION
PL/pgSQL — variables, control structures, exception handling
Triggers — automation for data quality
ER modelling, normalization (1NF, 2NF, 3NF)
OLTP vs analytics workload patterns
pgvector extension — vector similarity search inside PostgreSQL (preview of
Section 7 RAG)
Query plan analysis with EXPLAIN and EXPLAIN ANALYZE
Index strategies — selectivity, covering indexes, multi-column indexes
VACUUM, ANALYZE, partitioning
AI engineers read query plans daily — invest in EXPLAIN ANALYZE fluency
04
Python Libraries for AI
Essential Python libraries every AI engineer needs daily. Four modules covering
the core data manipulation and HTTP libraries — lighter than the full Data Science Python Libraries
track, focused on AI engineering essentials.
N-dimensional arrays — the foundation of numerical Python
Array creation, indexing, slicing
Vectorised operations and broadcasting
Why vectorisation is 100x faster than Python loops
Linear algebra basics with np.linalg
Embedding vectors are NumPy arrays — fluency matters
Series and DataFrame structures
Data loading — read_csv, read_excel, read_sql, read_json
Data cleaning — handling missing data, duplicates, type conversion
groupby operations — the split-apply-combine pattern
Merging, joining, reshaping
Time series basics
Use Pandas for data prep before AI training/inference, not for production AI
pipelines
requests library — HTTP for humans
GET, POST, PUT, DELETE patterns
Headers, authentication, sessions
Error handling and retries with tenacity
httpx — modern async HTTP client (preview of Section 6 FastAPI)
Essential for calling external AI APIs
pydantic — data validation and settings management (preview of FastAPI)
python-dotenv — environment variable management for API keys
loguru — better logging for AI applications
rich — pretty terminal output for AI agent debugging
tiktoken — OpenAI tokeniser for cost estimation
05
Advanced Python Concepts
Production-grade Python patterns for AI engineering. Four modules covering
async, packaging, testing, and the engineering practices that separate scripts from systems.
asyncio fundamentals — event loops, coroutines, tasks
async def and await patterns
Async context managers with async with
Async iterators with async for
asyncio.gather() for concurrent calls
httpx.AsyncClient for concurrent HTTP/AI API calls
Async is essential for AI engineering — every LLM call is I/O-bound
PEP 484 type hints
Generic types — List[int], Dict[str, Any], Optional[X], Union[X, Y]
typing module — Callable, Protocol, TypeVar, Generic
mypy for static type checking
Pyright / Pylance for IDE integration
Type hints are non-negotiable in production AI codebases
pyproject.toml — modern Python packaging
Building packages with uv / poetry / hatch
Project structure best practices for AI applications
Distribution patterns — PyPI, internal package indexes
Reusable AI components belong in packages, not copy-paste scripts
pytest fundamentals — fixtures, parametrize, markers
Mocking LLM calls with pytest-mock
Recording and replaying API calls with vcr.py / pytest-vcr
Async testing with pytest-asyncio
Coverage with pytest-cov
Testing AI applications requires mocking the non-deterministic AI calls — invest
in this pattern
06
Modern Python Framework FastAPI
FastAPI represents the next generation of Python web frameworks — combining
speed, modern Python features, and automatic documentation. Built on Starlette and Pydantic, it
delivers exceptional performance through asynchronous capabilities. Five modules taking you from
FastAPI fundamentals through production-grade authentication and database integration.
Environment Setup — create virtual environment, install FastAPI with Uvicorn ASGI
server
First Endpoint — define path operations using decorators: @app.get, @app.post,
@app.put, @app.delete
Run Server — launch development server with automatic reload for rapid iteration
3x Performance — FastAPI matches Node.js and Go performance
100% Type Safety — complete type hints enable automatic validation,
serialisation, and IDE support
Auto Documentation — Swagger UI and ReDoc generated automatically from code
annotations
Path parameters with type hints
Query parameters with defaults and Pydantic validation
Pydantic models for request bodies — automatic validation
Response models with response_model
Status codes and HTTPException patterns
Pydantic is FastAPI's killer feature — validation for free
SQLAlchemy ORM with dependency injection
Database session management patterns
CRUD operations through repository pattern
Alembic migrations — schema evolution for production
Async database access patterns
The production data layer for FastAPI services
APIRouter — organises code into modular components
Repository pattern — separates database logic from API logic
Environment variables for configuration (python-dotenv + pydantic-settings)
Layered architecture — routes → services → repositories → models
Docker containerisation for FastAPI
Production projects scale with proper structure — invest here
Bcrypt hashing — industry-standard password encryption
Salt and pepper patterns
Password complexity policies
JSON Web Tokens with expiration enable stateless authentication
Token claims (sub, exp, iat, aud)
Refresh token patterns
OAuth2PasswordBearer dependency verifies tokens
Extract current user information
Multiple OAuth2 flows
Authentication dependencies secure endpoints
Role-based access control (RBAC) manages permissions
Permission decorators
API key authentication alternatives
Section Project — build and deploy a production FastAPI service with PostgreSQL
backend, JWT auth, role-based access control, Pydantic validation, async endpoints, and full
Swagger documentation
07
Generative AI Deep Dive
The 2026 GenAI engineering layer. Ten modules covering everything from
Transformer architecture internals through frontier model mastery (GPT-5.5, Claude Opus 4.7, Gemini
3.1 Pro) to production RAG pipelines with vector databases. The depth that distinguishes AI
Engineers from API callers — and the differentiator the AI Engineer brochure builds the programme
around.
Narrow AI — image classifiers, speech recognition, pre-2022 era of task-specific
intelligence
Generative AI — LLMs, image/video/audio generation, post-2022 era unleashed by
ChatGPT
Agentic AI — Plan / Reason / Act / Learn loops, tool use, the post-2024 era of
autonomous systems
2022 inflection point — ChatGPT launch enters mainstream professional workflows
2024 inflection point — AI systems begin planning, using tools, completing
multi-step tasks autonomously
What's coming 2026-2030 — increasingly capable reasoning models, deeper tool
integration
Multi-agent collaboration at scale and continuous learning from real-world
feedback
LLM internals — Transformer architecture, attention mechanism, tokenisation
Frontier model landscape — GPT, Claude, Gemini, Llama, DeepSeek
Agent architecture — perceive, reason, act, learn loops
AI safety — alignment, hallucination, prompt injection
Workstation setup — Python, Jupyter, VS Code, API keys, billing
GPT-5.5 — The Autonomous Agent. First fully retrained base since GPT-4.5.
Natively omnimodal
GPT-5.5 — Terminal-Bench 2.0 leader at 82.7%, OSWorld-Verified at 78.7%
GPT-5.5 best for autonomous agents, computer use, terminal automation, multi-tool
orchestration
GPT-5.5 — 40% token efficiency gain over GPT-5.4 on Codex tasks. Pricing: $5/$30
standard, $30/$180 Pro
Claude Opus 4.7 — The Precision Coder. Hybrid reasoning model with extended
thinking mode
Claude Opus 4.7 — SWE-bench Pro leader at 64.3%, lowest hallucination rate on
long-form work at 36%
Claude Opus 4.7 — deepest native MCP support of any frontier model. Pricing:
$5/$25 (17% cheaper than GPT-5.5 on output)
Gemini 3.1 Pro — The Context Giant. Natively multimodal with 2M+ token context
window — largest of any frontier model
Gemini 3.1 Pro — GPQA Diamond leader at 94.3%, ARC-AGI-2 leader at 77.1%. Three
thinking levels: low/medium/high
Gemini 3.1 Pro best for high-volume batch jobs, multimodal media analysis,
cost-sensitive workloads. Pricing: $2/$12
Llama 4 (Meta) — strong general-purpose open-weights
DeepSeek — reasoning-focused, low cost
Mistral — European AI, GDPR-aligned, strong multilingual
Qwen — Alibaba's frontier-competitive open-weights
Intelligent Routing — Opus 4.7 as daily driver for writing and code review
GPT-5.5 for autonomous agents and terminal tasks; Gemini 3.1 Pro for
cost-sensitive and multimodal work
Mini/nano sub-models for high-volume orchestration
Microsoft Copilot Suite — Word, Excel, PowerPoint, Outlook, Teams integration
Copilot Studio — enterprise no-code agent building
Perplexity — citation-grounded AI search
NotebookLM — long-document analysis with audio overview
ChatGPT Codex — agentic coding environment
Zero-shot prompting — instruction-only patterns
Few-shot prompting — example-driven prompts
Chain-of-Thought (CoT) — step-by-step reasoning
ReAct (Reasoning + Acting) — interleaved thought and action
Tree-of-Thought — exploring multiple reasoning paths
System prompts — persona and constraint design
Output formatting — JSON, XML, structured outputs
Prompt chaining — multi-step LLM workflows
Context Engineering — the 2026 frontier discipline; what separates it from prompt
engineering
Context window optimisation — what to include, what to summarise, what to
retrieve
Memory patterns — short-term, long-term, episodic
Tool result formatting for agent context
Domain prompts for AI Engineering work — API design reviews, code refactoring,
debugging
Project — build a 30+ portfolio prompt library across zero-shot, few-shot, CoT,
ReAct, Tree-of-Thought patterns
ChatGPT, Claude, Gemini for daily AI engineering work
AI for code writing, code review, debugging, refactoring
Research with Perplexity for technical deep dives
NotebookLM for long-document analysis
Microsoft Copilot integration for Office productivity
AI engineers use AI tools 10x more than other engineers — master this stack
Vision models — GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro vision capabilities
Image generation — DALL-E 4, Midjourney v7, Stable Diffusion XL, FLUX
Audio models — Whisper for transcription, ElevenLabs for synthesis
Video models — Sora 2, Veo 3, Gemini 3.1 video understanding
Multimodal embeddings — CLIP-style cross-modal retrieval
Building multimodal AI applications
Hallucination — when an LLM invents facts that don't exist
Prompt injection — direct and indirect injection attacks
Privacy — keeping sensitive data out of public LLMs
Bias and fairness — recognising and mitigating model biases
Regulatory landscape — EU AI Act, India DPDP Act, NIST AI RMF
Output filtering and content moderation
Production AI engineers are responsible engineers — invest here
Streamlit — rapid prototyping for AI demos
Gradio — Hugging Face-native AI interfaces
FastAPI + React — production-grade AI applications (building on Section 6)
Chatbot patterns — single-turn vs multi-turn, state management
Streaming responses for better UX
Build and deploy a Streamlit + FastAPI AI application
OpenAI Python SDK — chat completions, function calling, structured outputs
Anthropic Python SDK — messages API, tool use, extended thinking
Google GenAI Python SDK — Gemini API patterns
DeepSeek Python SDK — cost-efficient alternative
Function calling and structured outputs (Pydantic-validated)
Streaming with async iteration
Retry logic with exponential backoff
Cost tracking with tiktoken and provider usage APIs
Rate limiting and queueing
Caching patterns for repeated prompts
OpenAI text-embedding-3-large — the default choice
Voyage AI — high-performance alternative
Cohere embed models
Open-source — Sentence Transformers, BGE, E5
ChromaDB — local/dev embedded database
Pinecone — managed serverless vector DB
Qdrant — open-source production-grade
pgvector — PostgreSQL extension (continuing from Section 3)
Weaviate, Milvus, LanceDB — alternatives
HNSW (Hierarchical Navigable Small World) indexing
IVF (Inverted File) indexing
Distance metrics — cosine, Euclidean, dot product
Filtering with metadata
The canonical RAG flow — Chunk → Embed → Index → Retrieve → Augment → Generate
Fixed-size chunking with overlap
Semantic chunking — embedding-based segmentation
Hierarchical chunking — parent-child relationships
Recursive character text splitting
Dense retrieval — embedding similarity
Sparse retrieval — BM25 keyword matching
Hybrid search — BM25 + embeddings combined
Re-ranking with cross-encoders (Cohere Rerank, BGE Reranker)
Agentic RAG — self-improving retrieval with agents that refine queries
Multi-hop RAG — chaining retrievals for complex questions
Graph RAG — knowledge graph-augmented retrieval
MCP-enhanced RAG — preview of Section 8
Retrieval metrics — recall@k, MRR, NDCG
Generation metrics — RAGAS, faithfulness, answer relevance
End-to-end evaluation with LLM-as-judge
Section Project — build a production RAG service over a domain corpus (technical
documentation, codebase, or knowledge base) with hybrid search, re-ranking, and full evaluation,
deployed via FastAPI
08
Agentic AI Deep Dive + Coding Agent Project
The 2026 flagship — and the section that produces your brochure-named Coding
Agent project. Ten modules covering the complete production agentic AI stack: LangGraph 1.0, Claude
Agent SDK, CrewAI, Pydantic AI, Model Context Protocol (MCP), agent design patterns, multi-agent
orchestration, observability, and the deployed AI Coding Agent that closes every 2026 AI Engineer
interview.
Perceive → Reason → Act → Learn loop
Tools and tool use — the foundation of agency
State and memory — what agents remember
Plans and goals — autonomy vs orchestration
The post-2024 emergence of autonomous AI systems
Industry adoption — code generation, research, customer service, security
Every AI Engineer in 2026 ships at least one agent
LLM (the brain) — typically GPT-5.5, Claude Opus 4.7, or Gemini 3.1 Pro
Tools — functions the agent can call
Memory — short-term context, long-term storage
Planner — decomposes goals into steps
Executor — runs the plan with tool calls
Observer — handles results, errors, loops
#1 ranked production agent framework for 2026 — stateful, graph-based agent
workflows with first-class support for cycles, branching, and human-in-the-loop checkpoints
LangGraph fundamentals — nodes, edges, state
StateGraph — typed state with TypedDict / Pydantic
Conditional edges for branching logic
Cycles and loops — what LangGraph does better than DAG frameworks
Checkpointing — pause and resume agent state
Persistence — durable agent state with SQLite/Postgres
Streaming agent intermediate steps
Human-in-the-loop patterns for production safety
Build your first LangGraph agent — a Q&A agent with retrieval, tool use, and
human approval
#2 ranked framework — Anthropic's official agent SDK with the deepest native MCP
integration of any agent framework
Claude Agent SDK Python library
Subagents and orchestration patterns
Native MCP integration — connect to any MCP server with zero glue code
Computer use — browser and OS automation through Claude
Extended thinking integration for complex reasoning
Streaming agent execution
Build an agent with Claude Agent SDK + 3 MCP servers (filesystem, GitHub,
postgres)
#3 ranked framework — role-based multi-agent collaboration
CrewAI fundamentals — agents, tasks, crews
Role-based agent design — Researcher, Writer, Reviewer, Tester patterns
Task delegation between agents
Sequential vs hierarchical crew patterns
Agent collaboration — when crews outperform single agents
Build a 3-agent crew for a software engineering task
Pydantic AI — type-safe agent design from Pydantic team
Structured outputs with Pydantic models
Type-safe tool definitions
Dependency injection patterns for agents
Validation guarantees — production-grade reliability
Type safety transforms agent reliability — invest here
The open standard reshaping agent infrastructure — originally created by
Anthropic, now stewarded by the Linux Foundation with 200+ server implementations and 97M+ monthly
SDK downloads
What MCP is — open protocol for connecting AI agents to tools, data, and systems
Architecture — MCP hosts, MCP clients, MCP servers
Transport — stdio, SSE, WebSocket
Primitives — Resources, Tools, Prompts, Sampling
Python SDK — mcp package
Defining tools, resources, prompts
Server lifecycle and capabilities
Build an MCP server exposing a custom tool
200+ community servers — filesystem, GitHub, Postgres, Slack, Linear, etc.
Official Anthropic servers — the reference implementations
Enterprise MCP servers — building internal tool access for agents
Security — authentication, scopes, sandboxing
Performance — caching, batching, async patterns
Observability — tracing MCP calls
ReAct — Reasoning + Acting interleaved
Plan-and-Execute — separate planning from execution
Reflection loops — agent reviews its own work
Self-consistency — multiple reasoning paths, vote on outcome
Tool calling — function calling at scale
Supervisor pattern — one agent orchestrates many
Swarm pattern — peer agents collaborate
Debate pattern — agents argue to refine answers
Hierarchical — managers and workers
Human-in-the-loop checkpoints — required for production
Confidence thresholds — autonomous vs human-routed
Approval gates for destructive actions
Audit trails for every agent action
A2A Protocol — agent-to-agent communication standard
Message formats and routing
Discovery and capability negotiation
Inter-agent trust and authentication
When to use A2A vs MCP
LangSmith — agent tracing and evaluation platform
Trace analysis — debugging multi-step agent runs
Token usage tracking and cost attribution
Evaluation datasets — agent regression testing
Prompt versioning and A/B testing
Helicone, Langfuse, Arize Phoenix — observability options
OpenTelemetry for agent tracing
Deployment patterns — Docker, Kubernetes, serverless
Scaling agents — concurrency, queueing, batch processing
Cost optimisation — model routing, caching, batch APIs
Incident response for agent failures
Agents in production need ops discipline — invest in observability
The brochure-named final project — a deployed multi-agent AI Coding Agent that
demonstrates production AI engineering competency across every previous section
LangGraph state machine for stateful coding workflows
Claude Agent SDK for deepest MCP integration
CrewAI multi-agent collaboration — Code-Writer Agent generates Python code from
natural language
Code-Reviewer Agent reviews generated code for quality, security, style
Tester Agent writes and runs pytest tests
Documenter Agent generates docstrings and README updates
Filesystem MCP server — read/write code files
Git MCP server — version control operations
GitHub MCP server — PR creation, issue management
Postgres MCP server — code metrics, history
Python execution MCP server — run tests, validate code
Generate Python code from natural language requirements
Refactor existing code with safety guarantees
Debug failures by reading error messages and proposing fixes
Test generated code with pytest
Document code with docstrings and README updates
Commit and PR with proper message conventions
FastAPI backend (from Section 6) serving the agent
Streamlit or React frontend for user interaction
PostgreSQL (from Section 3) for agent state and history
JWT/OAuth2 auth (from Section 6)
LangSmith observability for every agent run
Human-in-the-loop checkpoints before every commit
Cost tracking and budget enforcement
Public verification URL with the 2026 Agent-Ready rubric
A deployed AI Coding Agent accessible via public URL, GitHub repository with full
source code, demo video, LangSmith dashboard, and architecture decision records — the named
project for the entire AI Engineer programme
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 · weeks 3–12
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 · weeks 6–11
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 · weeks 8–12
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 · weeks 11–12
Your AI agent system in a real partner org.
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 partner orgs, weekly milestones, and grading rubric — PDF, 14 pages.
2026: 220+ deployed76% → placement offers
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Lync · 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 cohort."
15 yrs
AI ENGINEERING
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync 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, Digital Lync · 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 Digital Lync, 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 Digital Lync, 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 Digital Lync grads.
Real feedback from engineering leaders at AI labs and the firms hiring our AI Engineer · GenAI & Agentic graduates.
Digital Lync 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+ Digital Lync 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 Digital Lync 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
Digital Lync'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+ Digital Lync 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. Digital Lync 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+ Digital Lync graduates for our GenAI engineering practice. Strong on LangGraph, sharp on MCP/A2A, fluent in agent eval.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync 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.
Digital Lync · 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 · Digital Lync
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.
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) 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 AI Engineer cohorts running on IST and PST. Every online cohort 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
Next class
25 May 2026
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. Weeks 1–2 cover the GenAI fundamentals, LangGraph patterns, and agent design from scratch. What you do need: consistency and 12–15 hours a week.
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.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours building your agent system, and ~5 hours of project work (LangGraph, MCP, 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 AI engineers?
Yes — a dedicated placement advisor from week 8, not a helpdesk. AI product hiring partners include Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, and Postman. 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 — 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 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 AIE-022 starts 1 Jun 2026.
40 seats. 12 already claimed.
Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real projects from class 022.
