Production RAG systems
Retrieval pipelines that scale to millions of documents — chunking, hybrid search, reranking, citation-by-default prompting.
92% accuracy · 100K+ docs
Available · June 2026I architect AI systems
I architect AI systems
that ship.
15+ years building production software · 3+ years shipping enterprise LLM systems for government. RAG, NL-to-SQL, document intelligence — the kind that runs 24/7, not the kind that demos well.
▸
Cut document research time from 2 hours to 10 seconds.
Built an AI chatbot now handling 18K+ queries a month.
Made 200+ non-technical staff fluent in SQL — without SQL.
Shipped a vision pipeline saving 2,000 staff hours every month.
Reduced GPT-4 API costs by 38% while improving response quality.
Ask my AI anything
Mazhar HayatAI Solutions Architect // Senior Engineer
Agentic AIMulti-Agent OrchestrationAutoGenLangGraphCrewAITool UseFunction CallingReActReflexionMCPA2A ProtocolsRAGGraphRAGHybrid SearchRe-rankingSemantic CachingEvals & GuardrailsPrompt EngineeringFine-tuningLoRA / QLoRAVision AINL-to-SQLGPT-4oGPT-4.1Claude 3.5 SonnetGemini 1.5 ProLlama 3.1MistralGroqAzure OpenAILangChainLlamaIndexSemantic KernelPineconeWeaviateFAISSAzure AI Search.NET Core 8Angular 17KubernetesDocker15+ Years100K+ Docs Indexed18K+ Monthly Queries95% Time ReductionAgentic AIMulti-Agent OrchestrationAutoGenLangGraphCrewAITool UseFunction CallingReActReflexionMCPA2A ProtocolsRAGGraphRAGHybrid SearchRe-rankingSemantic CachingEvals & GuardrailsPrompt EngineeringFine-tuningLoRA / QLoRAVision AINL-to-SQLGPT-4oGPT-4.1Claude 3.5 SonnetGemini 1.5 ProLlama 3.1MistralGroqAzure OpenAILangChainLlamaIndexSemantic KernelPineconeWeaviateFAISSAzure AI Search.NET Core 8Angular 17KubernetesDocker15+ Years100K+ Docs Indexed18K+ Monthly Queries95% Time Reduction
Where I've shipped
SCADStatistics Centre Abu Dhabi
MoHREUAE Government
NETSOLAsset Finance Platform
TRG TechSentiment Engine
What I build with
- Azure OpenAI
- Pinecone
- Anthropic
- Cohere
- Groq
- LangChain
Logos shown are the property of their respective owners and used here solely to identify past clients and employers. No endorsement or partnership is implied.
Shipped systems for · Trained by
SCAD
Statistics Centre Abu Dhabi
MoHRE
Ministry of Human Resources · UAE
Microsoft AI Developer Program
Trained · Aug–Sep 2025
15+ Years
Shipping production software
/ about
Engineer first. Architect second.
I've spent 15+ years shipping software — from leasing platforms in Lahore to government services in Abu Dhabi to LLM systems running 24/7 today.
The pattern that kept showing up: the hard part is never the model. It's the chunking, the retrieval, the prompts, the evals, the cost. The boring stuff between the demo and the system.
I now spend my days on production LLM architecture — RAG pipelines over 100K+ documents, NL-to-SQL across eight databases, vision pipelines that save thousands of staff hours a month. The work is technical, opinionated, and end-to-end.
Currently · Abu Dhabi, United Arab Emirates. Available from June 2026 for senior IC, principal, or hands-on tech-lead roles.
0+
Years Shipping Software
0+
Production AI Systems
0K+
Documents Processed
0K+
Monthly AI Queries
0%
Research Time Saved
0K+ hrs
Staff Hours Saved / Mo
evidence over claims ✓
/ operating principles
The four rules I actually work by.
- 0101
Production beats prototype.
A demo is a hypothesis. Shipped software in front of real users is the only reliable signal.
- 0202
Architecture is a forcing function.
Choose the system shape that makes the right thing easy and the wrong thing visible.
- 0303
Measure or it didn't happen.
Latency, accuracy, cost, satisfaction — define them, instrument them, then iterate on numbers.
- 0404
AI is plumbing, not magic.
Retrieval, evaluation, guardrails, observability — the boring layers are what make the magic work.
Lesson · 02
“The bottleneck is never the model — it's chunking, retrieval, and prompts.”
/ systems in production
Three pipelines, drawn exactly how they run.
Not marketing diagrams — these are the actual shapes of systems running today at government scale. Hover any node for what it really does.
rag.pipeline
liveLesson · 01
“A demo is a hypothesis. Production is the only evidence.”
/ services
What I do, and how I do it.
Conversational AI
NL-to-SQL, copilots, multi-turn agents. Schema injection, execution-aware repair loops, multilingual (Arabic + English).
85%+ SQL accuracy · 200+ users
Document intelligence
Computer-vision pipelines with confidence-gated human review. Type-aware routing to the cheapest extractor that works.
2,000+ hrs/month saved
LLM evaluation harnesses
Gold-set evaluation, regression tracking, cost-aware model routing. The boring stuff that turns demos into systems.
Evals are the new unit tests
Prompt + cost engineering
Few-shot, chain-of-thought, function calling. Caching, model routing, token budgeting — without hurting quality.
38% GPT-4 cost cut
Technical leadership
Mentor mid-level engineers, run AI architecture reviews, write the docs everyone actually reads.
15+ yrs, 5+ teams led
/ portfolio
Selected work, production grade.
Four flagship systems. Each one was an experiment for someone, somewhere. Now they all run 24/7 in front of real users.
2025 · SCAD
FeaturedEnterprise RAG Document Intelligence System
Architected end-to-end RAG pipeline using Azure OpenAI and Cognitive Search with hybrid retrieval and re-ranking.
- Reduced information retrieval time from 2–3 hours to under 10 seconds
- Achieved 92% accuracy on complex multi-document queries
- Processing 5K+ queries monthly with 87% user satisfaction
GPT-4Azure Cognitive SearchLangChainPineconeAzure Functions
Full case study2023 – Present · SCAD
FeaturedIntelligent Conversational Analytics Platform
Built natural language to SQL query system with conversational interface and automatic error correction.
- Enabled 200+ non-technical staff to query databases using plain English
- Handles 15K+ queries monthly across 8 different databases
- Reduced analytics request backlog by 70%
GPT-4Semantic KernelAzure OpenAI.NET Core Web APIAngular
Full case study2025 · SCAD
FeaturedDocument Processing & Vision AI Pipeline
Built intelligent document processing pipeline using GPT-4 Vision and Azure Form Recognizer.
- Automated 80% of document classification and data extraction tasks
- Reduced processing time from 15 minutes to 30 seconds per document
- 94% accuracy on structured form extraction
GPT-4 VisionAzure Form RecognizerAzure FunctionsBlob StorageCosmos DB
Full case study/ stack
The toolkit behind the systems.
Green chips link to a project or article where I actually used the thing — proof, not just labels.
01
Generative AI & LLMs
Building with frontier models in production.
02
RAG & Vector Search
Retrieval pipelines that scale to millions of docs.
Multi-stage RetrievalHybrid SearchRe-rankingPineconeFAISSChromaAzure Cognitive SearchWeaviateOpenAI EmbeddingsSentence Transformers
03
AI Frameworks
Orchestrating agents, tools, and memory.
04
Cloud & Architecture
Secure, event-driven, cloud-native platforms.
Azure OpenAIAzure FunctionsAzure API ManagementCosmos DBAzure DevOpsDockerKubernetesMicroservicesEvent-Driven Architecture
05
Full Stack
End-to-end systems from API to UI.
06
Conversational AI & NLP
Natural dialogue that resolves real problems.
Multi-turn DialogueIntent ClassificationEntity ExtractionSentiment AnalysisDocument UnderstandingSummarizationQ&A Systems
/ experience
git log --author="mazhar"
Nov 2022 – Present
Abu Dhabi, UAE
Senior System Analyst / AI Platforms
@ Statistics Centre — Abu Dhabi (SCAD)
- Architected enterprise RAG system processing 100K+ documents using GPT-4 and Azure Cognitive Search, reducing research time by 95%
- Deployed conversational AI chatbot handling 18K+ monthly queries with 90% first-contact resolution, cutting support costs by 43%
- Built natural language SQL query interface enabling non-technical users to access 8 databases, processing 15K+ queries monthly
- Designed prompt engineering framework reducing GPT-4 API costs by 38% while improving response quality by 15%
- Led migration of 8 legacy monolithic applications to AI-enhanced microservices
GPT-4Azure OpenAILangChainSemantic KernelPinecone.NET Core 8Angular 17Kubernetes
July 2018 – Nov 2022
UAE
Senior Full Stack Engineer / Team Lead
@ Ministry of Human Resources & Emiratisation (MoHRE)
- Led development of Tasheel Systems — 50+ labor and HR services applications serving 2M+ users annually
- Modernized legacy codebase to microservices architecture, improving performance by 45%
- Designed RESTful APIs consumed by 30+ internal and external systems with OAuth 2.0
- Implemented Redis caching strategy reducing database load by 55%
- Led team of 6 developers using Agile/Scrum with 95%+ sprint completion rate
.NET Core 5/6Angular 12-14ReactDockerKubernetesAzure DevOpsRedisAWS
June 2015 – June 2018
Lahore, Pakistan
Senior Software Engineer / Technical Lead
@ TRG Tech
- Led cross-functional team of 8 developers (Full Stack, iOS, Android)
- Built real-time sentiment analysis engine processing 100K+ social media posts daily
- Developed social media monitoring platform integrating Twitter, Facebook, Instagram APIs
- Architected data pipelines processing 1M+ records daily for business intelligence
.NET Framework 4.6AngularNode.jsSQL ServerSocial Media APIsSentiment Analysis
Dec 2012 – June 2015
Lahore, Pakistan
Software Developer
@ NETSOL Technologies
- Maintained and enhanced large-scale financial leasing suite for international clients (FIAT, CNH Industrial)
- Delivered 20+ features for enterprise financial management system
- Reduced bug count by 35% through code refactoring and unit testing
.NET Framework 4.5ASP.NET MVCAngularJSSQL ServerCrystal Reports
/ training & programs
Trained by Microsoft. Applied in production.
Microsoft Official Course completions and self-paced learning paths. I haven’t sat the AI-102 / AZ-305 exams yet — the knowledge is applied daily in production at SCAD.
Trained
Azure AI Engineer track (AI-102)
Training completed · exam not yet attempted
Worked through the full AI-102 curriculum — Azure OpenAI, Cognitive Services, knowledge mining, conversational AI — applied directly in production at SCAD.
Microsoft Learn
Trained
Azure Solutions Architect track (AZ-305)
Training completed · exam not yet attempted
Self-paced study of architecture design patterns for Azure, identity, governance, data platform, and business-continuity design.
Microsoft Learn
/ live demo
This site runs RAG on itself.
Ask anything about my CV and watch the full retrieval pipeline run in real time — embed, retrieve, rerank, generate with citations. All free-tier infrastructure.
01
Embed
Your question becomes a 3072-dim vector with Gemini Embedding 001.
02
Retrieve + Rerank
Pinecone fetches top-10 candidates; Cohere rerank-v3.5 reorders them.
03
Cited generation
Groq Llama 3.3 70B synthesises a cited answer from the top-5 chunks in ~1 s.
16 chunks · Pinecone serverless · Gemini · Cohere · Groq · $0/month
Live RAG Demo
Pinecone · Gemini · Cohere · Groq Llama 3.3
Live
new · Model Context Protocol
Talk to my portfolio from Claude.
I shipped an MCP server that exposes my profile, projects, skills and CV corpus as live tools. Any MCP-compatible client (Claude Desktop, Cursor, etc.) can query, search and check fit programmatically.
get_profile
Bio, role, location
list_projects
All shipped projects
search_cv
Semantic search the corpus
Lesson · 03
“Measure or it didn't happen. Latency, accuracy, cost — define them first.”
/ testimonials
What the people who shipped with me say.
“Mazhar architected our RAG document intelligence system from scratch — 100K+ government documents, retrievable in seconds. His ability to translate a vague business problem into a precise, production-ready AI architecture is rare. He delivered on time, measured everything, and the system has run without issues for over a year.”
Senior Director
Digital Transformation · Statistics Centre Abu Dhabi (SCAD)
Direct stakeholder
“He built the NL-to-SQL analytics platform that changed how 200+ of our non-technical staff work. What impressed me most wasn't the technology — it was his insistence on measuring accuracy before and after every change. He doesn't ship until the numbers say it's ready.”
Head of Analytics
Data & Analytics · SCAD
Internal client
“Mazhar led the backend architecture for Tasheel — one of the highest-traffic government service platforms in the UAE. He brought the kind of calm, systematic thinking that made a complex distributed system feel simple. His code reviews alone upskilled the entire team.”
Engineering Manager
Platform Engineering · MoHRE UAE
Direct manager
/ writing
Field notes from shipping AI.
Real architectures, real numbers, hard-won lessons. No fluff.
Nov 2025 12 min readcoming soon
RAG from prototype to production: what nobody tells you
Chunking strategies, re-ranking, hybrid search, eval frameworks — the six decisions that separate a demo from a system that runs 24/7 in front of thousands of users.
RAGLangChainAzure OpenAIProduction
Sep 2025 9 min readcoming soon
Prompt engineering patterns I actually use in production
Few-shot, chain-of-thought, function calling, and system-prompt hygiene — with real examples from the systems I've shipped and the cost/quality trade-offs of each.
GPT-4Prompt EngineeringAzure OpenAI
Jul 2025 10 min readcoming soon
Getting NL-to-SQL to 85%+ accuracy without fine-tuning
How schema injection, intent classification, execution-aware repair loops, and a good evaluation harness got our conversational analytics platform to production-grade accuracy.
Semantic KernelSQLGPT-4NLP
May 2025 7 min readcoming soon
Cutting GPT-4 API costs 38% without hurting quality
The prompt engineering framework we built at SCAD — caching, token budgeting, model routing, and eval-driven iteration — that saved tens of thousands annually.
Cost OptimisationGPT-4LLMOps
/ newsletter
New articles & shipped projects — straight to your inbox.
~1 email a month. Real lessons from production AI. No spam, no recycled LinkedIn content.
/ contact me
Come on let's talk.
The fastest path is a 15-minute Calendly call — no slide deck, no prep, just questions about your problem and whether I'm the right person for it.