Hands-on every week
Each week ends in a lab you build, not a slide deck to watch.
DATA & AI EVANGELIST · SOLUTION ARCHITECT
Build AI that survives production.
Hands-on training and engineering case studies from Mohammad Dawood Hussain, who builds agentic AI, RAG, and MCP platforms for regulated enterprises.
- 12 Weeks
- 6–8 Hours / Week
- Python 3.11+
- Applied Labs & Capstone
DATA & AIEVANGELIST
- Python
- FastAPI
- LangGraph
- Amazon Bedrock
- Azure OpenAI
- LangSmith
- MCP
- RAG
- .NET
- C#
- React
- AWS
- Azure
- PostgreSQL
- pgVector
- Docker
- Kafka
- Redis
- FastAPI
- Node.js
- TypeScript
- Microservices
THE NUMBERS / A CAREER OF OUTCOMES
Engineering is a
practice of proof.
12K+
annual engineering hours eliminated through distributed validation automation
78%
critical API response-time reduction through performance engineering (32s → 7s)
30+
enterprise manufacturing databases made accessible through natural language
95%
right-first-time quality enabled with automated recipe validation
WHY THIS COURSE
Learn by building, not by watching.
One project, all the way through
A single integrated AI application grows from your first LLM call to a deployed service.
Production tooling
LangChain, LangGraph, MCP and FastMCP, LangSmith, Langfuse, pytest, and Docker.
Taught from real delivery
Grounded in production work in regulated pharmaceutical and energy environments.
HOW IT WORKS
Four stages, one system.
Learn
LLM foundations, prompt engineering, FastAPI, and LangChain. Weeks 1–4.
Build
RAG pipelines and LangGraph agents with human approval. Weeks 5–8.
Evaluate
Tracing and evaluation with LangSmith and Langfuse, plus pytest suites.
Ship
MCP tools, Docker packaging, and CI/CD for a deployable capstone. Weeks 9–12.
THE COURSES
Choose your next step.
Enrolling01Generative AI & Agentic AI EngineeringFrom Fundamentals to ProductionDesign, build, evaluate, and deploy real-world AI applications through hands-on engineering. One integrated capstone project, built progressively across twelve weeks.VIEW CURRICULUM →02COMING SOONAdvanced AI Engineering ConceptsFine-Tuning, Managed AI Services & NLPA focused program on production AI depth: LLM fine-tuning with LoRA, QLoRA, and PEFT; AWS Bedrock, AgentCore, and SageMaker; and applied NLP. VIEW CURRICULUM →
THE 12-WEEK JOURNEY
Three phases, twelve weeks.
Phase I · Weeks 1–4
Foundations, Application Architecture & Retrieval
Phase II · Weeks 5–8
RAG Systems & Agentic Architectures
Phase III · Weeks 9–12
Tool Protocols, Production & Capstone
CAPABILITY BRIEF
From strategy to
production systems.
AI & GenAI
An interactive learning roadmap — from software engineering foundations to production AI systems.
Cloud platforms
AWS, Azure, containerized services, CI/CD, high availability, and observability.
System design
Microservices, event-driven architecture, APIs, resilience, and performance.
Technical leadership
Architecture reviews, stakeholder alignment, delivery strategy, and mentorship.
SELECTED WORK / CASE STUDIES
Agentic systems, shipped to production.
Agentic AI
AI Recipe Authoring Agent
Production-grade agentic AI platform that reduced pharmaceutical recipe authoring from 4 months to 10 days, with human-in-the-loop validation and full traceability.
LangGraph · Amazon Bedrock · RAG · MCP · LangSmith Manufacturing Systems
EMES Regression & Load Testing Platform
Distributed regression and load-testing platform executing automated validation across multiple MES environments — eliminating 12,000+ engineering hours annually.
C# · .NET · SQL Server · PostgreSQL · Kafka AI-Assisted Engineering
RAG Test Script Generator
AI-assisted test script generation for regulated software validation, built on retrieval over historical validation assets — cutting validation effort from hours to minutes.
Python · RAG · Azure OpenAI · Vector Search · PostgreSQL Enterprise AI Infrastructure
MCP Semantic Retrieval Platform
MCP-based semantic retrieval platform letting AI agents discover and retrieve enterprise configurations through vector similarity search over PostgreSQL/pgvector.
FastMCP · MCP · PostgreSQL · pgVector · AWS AuroraPROFILE / 2026
Cloud architecture to AI orchestration.
From cloud-native platform architecture to query tuning and AI orchestration, I design the connective tissue that helps teams operate with more certainty — translating business problems into systems that hold up in production.
- Languages — C#, .NET, ASP.NET MVC, ASP.NET Core, Python, FastAPI
- AI & GenAI — Agentic AI, LangGraph, Amazon Bedrock, Azure OpenAI, LangSmith, MCP
- AI Engineering / LLMOps — Agent Orchestration, Stateful Workflows, Retrieval Strategies, Human-in-the-Loop, AI Observability, CI/CD
- Cloud — AWS, Azure, Docker, ECS, Azure DevOps, GitHub Actions
ENGINEERING / A STRUCTURED KNOWLEDGE BASE
From roadmap to production architecture.
Learning Roadmap
AI & GenAI
An interactive learning roadmap — from software engineering foundations to production AI systems.
Knowledge Base
System Design
Distributed systems, event-driven architecture, APIs, resilience, and scale.
Knowledge Base
Cloud
AWS, Azure, containerized services, CI/CD, high availability, and observability.
Knowledge Base
Software Engineering
Backend craft, performance engineering, testing, and code quality.
LATEST WRITING
What I learned building agents and RAG.
FEB 2026
What I Got Wrong About RAG
Three retrieval mistakes I made building enterprise RAG systems — and the fixes that actually moved quality.
READ ARTICLE →JAN 2026
What I Learned Building Agentic AI Systems
Lessons from taking agentic platforms to production in regulated manufacturing — why the graph matters more than the model, and where autonomy actually pays.
READ ARTICLE →ENGINEERING JOURNEY
From enterprise engineering to AI platform leadership.
Sep 2024 — Present
Hyderabad, India
Senior Software Engineer / Manager
Bristol Myers Squibb
Architecting enterprise AI platforms, distributed backend systems, and digital manufacturing solutions supporting global pharmaceutical operations — Enterprise MES.
Mar 2022 — Aug 2024
Hyderabad, India
Technical Lead
Cognizant Technology Solutions
Led the architecture and development of cloud-native backend platforms for large-scale energy forecasting, utility analytics, and enterprise digital transformation initiatives serving North American utility providers.
Sep 2014 — Feb 2022
Software Engineer
Cognizant Technology Solutions
Developed enterprise applications across healthcare, finance, insurance, and utility domains using Microsoft technologies while progressively taking ownership of architecture, backend design, and technical leadership.
FREQUENTLY ASKED
Questions, answered.
Who is this course for?
Working software engineers who want to build LLM applications, RAG pipelines, and tool-using agents. Working knowledge of Python, REST APIs, HTTP, and JSON is expected.
Do I need a GPU or a deep-learning background?
No. The course is built around LLM APIs and application engineering, so no GPU or prior deep-learning experience is required.
How much time does it take?
Plan for 6–8 hours per week across 12 weeks.
What do I build?
One integrated AI application that grows week by week, using mock business data and simulated actions.
How do I enroll?
Email dhacademyhyderabad@gmail.com for enrollment and cohort details. This site does not take payments.
READY TO BUILD?