A PROFILE IN SOFTWARE LEADERSHIP

Making the complex work.

Mohammad Dawood Hussain is a Principal AI Engineer & Solution Architect building reliable intelligence for the enterprise — from agentic AI platforms to the distributed systems beneath them.

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12+ YEARS · AI PLATFORMS · DISTRIBUTED SYSTEMS · CLOUD

Agentic AI, LangGraph, RAG, MCP & LLM orchestration
Cloud-native platforms on AWS & Azure
Microservices, APIs & event-driven systems
Performance engineering & technical leadership

PRINCIPAL
AI ENGINEER

CAPABILITY BRIEF

From strategy to
production systems.

AI & GenAI

Agentic workflows, RAG, MCP, vector search, guardrails, and evaluation.

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.

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

SELECTED WORK / CASE STUDIES

Built for the hard problems.

Architecture case studies from production enterprise AI and distributed systems. Generalized for confidentiality — the engineering is real.

01 — 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 →

02 — 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 →

03 — 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 →

04 — ENTERPRISE AI INFRASTRUCTURE

SIRIUS MCP

MCP-based semantic retrieval platform letting AI agents discover and retrieve enterprise configurations through vector similarity search over PostgreSQL/pgvector.

FASTMCP / MCP / POSTGRESQL →

ALL PROJECTS →

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

PROFILE / 2026

A systems thinker with range.

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

How I think, written down.

Not encyclopedias — concept, trade-offs, production considerations, and my personal approach for each topic.

AI & GENAI

Agentic workflows, RAG, MCP, vector search, guardrails, and evaluation.

EXPLORE AI & GENAI →

SYSTEM DESIGN

Distributed systems, event-driven architecture, APIs, resilience, and scale.

EXPLORE SYSTEM DESIGN →

CLOUD

AWS, Azure, containerized services, CI/CD, high availability, and observability.

EXPLORE CLOUD →

SOFTWARE ENGINEERING

Backend craft, performance engineering, testing, and code quality.

EXPLORE SOFTWARE ENGINEERING →

LATEST WRITING

Notes from the field.

Experience-driven articles on building production AI systems.

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

Leadership, earned in the work.

From enterprise software engineering to technical leadership to AI platforms.

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