14+ years designing enterprise-grade solutions with RAG, LLMs, Azure Cloud, .NET Core & Angular — turning complex data into actionable intelligence.
public class ZainAbbasTahir
{
public string Role => "AI Technical Lead";
public int YearsExp => 14;
public List<string> Stack => new()
{
"RAG", "LLMs", "Azure",
".NET Core", "Angular",
"Microservices", "AI Foundry"
};
public async Task<Impact> DeliverAsync(
BusinessChallenge problem)
{
var solution = await BuildAI(problem);
return await DeployToAzure(solution);
}
}
I build intelligent, scalable, and predictive systems — designing advanced AI architectures using RAG, LLMs, Predictive AI, Sentiment Analysis, and AI Foundry platforms.
My approach combines microservices, event-driven design, and AI-powered automation to deliver enterprise-scale, cloud-native solutions. I excel in end-to-end software delivery — from solution architecture and API integration to CI/CD pipelines, cloud infrastructure, and automated workflows.
RAG, LLMs, Predictive AI, Sentiment Analysis & AI Foundry
Data Fabric, Service Bus, Data Factory, Copilot Studio
End-to-end software delivery & technical leadership
CI/CD, IaC (ARM, Terraform, Bicep), GitHub Codespaces
A comprehensive toolkit built over 14+ years of professional development
Real solutions built for real business challenges across AI, Cloud & Enterprise domains
Manual compliance reviews were slow, error-prone and impossible to scale across hundreds of policy documents.
Built an LLM-powered platform that auto-reads documents, flags regulatory gaps and generates structured audit reports — cutting review time by 70%.
Operations teams spent hours manually compiling data into reports, delaying decisions and wasting analyst time.
Delivered an LLM engine that ingests live operational data and auto-generates structured spot reports — reducing effort by 80%.
Employees wasted hours searching through thousands of documents to find answers buried in PDFs, Word files and reports.
Built a RAG-powered chat UI so users simply ask a question and get exact answers from the document library in seconds.
Repetitive internal workflows (approvals, lookups, handoffs) required constant human coordination across multiple systems.
Designed a multi-agent AI system that autonomously plans tasks, calls tools and completes cross-system workflows — freeing teams for high-value work.
Teams only discovered performance bottlenecks after users complained — reactive firefighting was costly and damaging to SLAs.
Built an ML monitoring platform that proactively detects degradation patterns and auto-recommends or applies fixes before users are impacted.
Sales teams spent 60% of their time on unqualified leads and generic outreach, resulting in low conversion and wasted effort.
Engineered an AI pipeline that scores prospects, crafts personalised outreach via LLMs and feeds qualified leads directly into the CRM.
Support teams were overwhelmed with repetitive queries, causing long wait times and high operational cost.
Deployed a RAG + LLM chatbot that handles 70% of queries autonomously, with full context awareness and 24/7 availability.
Business decisions were made on gut feeling — lack of forecasting led to inventory mismatches and missed revenue opportunities.
Built an Azure ML platform that forecasts demand, predicts churn and surfaces customer behaviour patterns in real-time Power BI dashboards.
Password-based logins were a security liability — brute-force attacks, credential sharing and resets created constant risk and friction.
Delivered a facial recognition + liveness detection system for passwordless, phish-proof enterprise authentication.
DHL's manual route planning failed to account for real-time traffic and capacity — drivers followed suboptimal routes, increasing cost and delays.
Architected an AI system that ingests live data and dynamically re-optimises delivery routes — cutting fuel costs and improving on-time delivery.
Customer feedback was scattered across emails, reviews and social media — no way to spot trends or act on dissatisfaction quickly.
Built an NLP pipeline that ingests multi-channel feedback and surfaces real-time sentiment trends in Power BI for immediate action.
Enterprise-grade multi-tenant DMS with role-based access, PDF viewing, email & WhatsApp sharing and audit trails.
Custom URL shortening service with click tracking, analytics dashboard, geographic insights and AdSense integration.
Language barriers blocked effective communication between global teams and users across regions.
Designed an NLP-based automated translation engine supporting multi-language pairs with context-aware accuracy.
Unstructured visual and textual content was impossible to categorise at scale without massive manual effort.
Built a deep learning pipeline that classifies images, converts image sequences to video summaries and categorises text content automatically.
Training food recognition models from scratch required huge labelled datasets and significant compute resources.
Applied transfer learning on pre-trained CNN models to build a high-accuracy food detection and classification system with minimal training data.
Clinicians lacked tools to identify high-risk patients early — diseases were often caught too late from fragmented medical histories.
Developed an ML model that analyses patient medical history to predict disease likelihood, enabling early intervention and preventive care.
AI models lacked transparency and tamper-proof audit trails — enterprises couldn't trust or verify AI-driven decisions.
Investigated and implemented a framework combining blockchain's immutable ledger with AI decision logging to create verifiable, trustworthy AI pipelines.
Hidden relationships between items, users and behaviours were invisible — businesses missed cross-sell and pattern opportunities.
Built an association analysis framework using graph-based algorithms to surface item relationships, behavioural patterns and recommendation signals.
Dirty, inconsistent data across systems caused downstream analytics failures and unreliable business reports.
Built automated ETL pipelines with validation, deduplication and cleansing rules — ensuring analytics and ML models always run on trusted, verified data.
Fraudulent transactions, system failures and security breaches went unnoticed until significant damage had already occurred.
Developed an ML-driven anomaly detection system with continuous monitoring, auto-alerting and self-improving accuracy over time.
Complete POS system with inventory management, barcode support, debt tracking, and detailed sales reporting.
Have a project in mind or want to explore collaboration opportunities?