👋 I'm Adnan Tariq A Generative AI Engineer

I specialize in building intelligent systems with Large Language Models, AI automation, and cutting-edge generative AI solutions. I help businesses transform with AI-powered workflows and intelligent applications.

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Achievements and Adventures

I combine AI engineering, product thinking, and implementation discipline to build systems that are not just impressive demos, but useful tools. My focus is on shipping AI products that are technically sound, practical, and easy for people to use.

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3.80: CGPA, BS Computer Science

2: Research papers

1: Kaggle hackathon build

3 mo: Industry AI internship

A systematic review of privacy-preserving federated deep learning (2018-2025), mapping how distributed training keeps raw data on-device across smart cities, buildings and healthcare, and comparing the privacy mechanisms that make it trustworthy.

Computer Modeling in Engineering & Sciences (CMES)

Review Article · Published 2026 · Tech Science Press

Recent Projects

A selection of AI systems I've built across LLM applications, retrieval, automation, and real-world problem solving.

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Yaad

A free web app that keeps every scholarship application, document, deadline and research paper in one place, and emails you before each deadline. AI assistants can connect to it, but nothing changes until you approve it.

Scholarship and research-paper tracker that works with your AI assistant

Built with Next.js 15, TypeScript, Convex, Clerk, Cloudinary and Resend on Vercel. Automatic reminder emails 30, 15 and 5 days before each deadline, a document vault linked to each application, paper tracking from draft to published, and an MCP server with human approval, plus a REST API, access tokens and signed webhooks for n8n.

Next.jsTypeScriptConvexClerkMCPResendVercel

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Scholarship season used to be 23 open tabs. Now it's one.

What I Can Help Build

I focus on practical AI engineering work, from intelligent product features to research-backed systems. These are the areas where I can contribute most effectively.

LLM Applications

Build AI products that turn large language models into useful assistants, copilots, and domain-specific tools.

Design chat interfaces, copilots, and AI assistants around real user tasks.
Connect model outputs to practical product flows, not isolated demos.
Shape prompts, guardrails, and UX for clearer, more reliable results.
Bridge AI capability with front-end and product implementation.
Focus on systems that are useful in production settings.

AI Agents & Automation

Create multi-step AI workflows that use tools, memory, and orchestration to complete meaningful tasks.

Design agents that can retrieve data, reason, and take action.
Automate repetitive knowledge and decision-support workflows.
Structure tool use and handoffs for dependable task execution.
Reduce manual effort with AI-powered process automation.
Prototype quickly while keeping the system grounded and usable.

RAG Systems

Build retrieval-augmented systems that connect language models to your documents, data, and knowledge base.

Ingest PDFs, documents, and structured sources into searchable pipelines.
Improve response quality with retrieval, chunking, and citations.
Ground answers in source material instead of guesswork.
Support research assistants, internal knowledge tools, and Q&A workflows.
Design systems that balance relevance, latency, and trust.

AI Product Prototyping

Turn AI ideas into working MVPs that demonstrate value quickly and provide a strong base for iteration.

Translate concepts into practical technical plans and working prototypes.
Combine AI logic with product UX for faster validation.
Ship demos that help teams test feasibility and user value.
Move from research ideas to implementation-minded experiments.
Build with an eye toward maintainability and next-step scaling.

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