AI Engineer · Islamabad, Pakistan
Building intelligent systems that bridge the gap between research and real-world impact. Specializing in LLM fine-tuning, RAG pipelines, and agentic architectures.
"I build AI systems that don't just work in notebooks — they work in the world."
I'm a BS Artificial Intelligence student at Szabist University, Islamabad, focused on the intersection of research and production-grade engineering. My work sits at the frontier where language models meet real business constraints — latency, cost, reliability, and actual ROI.
My research spans Self-Supervised Fine-Tuning of transformer-based models, agentic pipeline design, and prompt engineering that scales. I care about building things that last: composable, observable, and genuinely useful.
As a Microsoft Student Ambassador (Beta) and Aspire Leaders Program fellow (Harvard Business School, 2025 cohort), I've learned that the best AI work happens at the intersection of deep technical skill and strategic thinking.
AI / ML
Engineering
Research
Exploring advanced prompt engineering methods for LLMs with a focus on efficiency, reasoning, and adaptability. Designing strategies that accelerate business growth by unlocking new AI-driven capabilities, improving ROI through automation, and enabling companies to scale with data-driven insights. Actively solving real-time problems in productivity, communication, and intelligent systems integration.
Leading independent research in Self-Supervised Fine-Tuning of transformer-based models for enterprise adoption. Dual focus on computational efficiency and business value creation through strategic model optimization. Developing frameworks that deliver optimized workflows, reduced operational costs, and enhanced ROI — with applications across healthcare, finance, and manufacturing.
Research focused on Reinforcement Learning, AI Agents, RAG, and Multi-Agent Systems with an emphasis on maximizing business ROI. Developed expertise in solving real-time enterprise challenges by optimizing computational efficiency and reducing operational costs through innovative algorithmic strategies and scalable architectures.
Beta Tier — Microsoft
2025 Cohort — led by Harvard Business School
Microsoft for Startups Founders Hub
NLP, Multi-Disciplinary Transformers & Advanced GenAI
OpenAI · GitHub Copilot · Notion · Miro · and more
Excelerate — Sep 2025 – Present
Szabist University, Islamabad
Specialized in Reinforcement Learning, AI Agents, RAG, and Multi-Agent Systems. Research focused on maximizing ROI and business value through strategic AI implementation — optimizing computational efficiency and reducing operational costs through innovative algorithmic strategies and scalable architectures.
Let's build something
Open to research collaborations, AI engineering roles, and interesting problems.