Open to opportunities

Hammad Ali Tahir

@hammadalitahir

Message

I build production-ready AI systems—ML, NLP, and LLM fine-tuning—to deliver scalable RAG and classification.

Pakistan

Message

What I'm looking for

I’m seeking an ML Engineer, NLP Engineer, or AI Engineer role where I can build and deploy production-ready AI systems at scale—shipping reliable RAG/LLM solutions with measurable accuracy, low latency, and strong engineering quality.

I’m a final-year BS Computer Science student specializing in ML, NLP, and LLM fine-tuning. I build production-grade AI systems end-to-end—modeling, RAG pipelines, and deployment—so teams can get accurate, reliable results in real workflows.

At Uraan AI Techathon 1.0, I built an AI LegalTech SaaS with intelligent case classification, automated triage, and semantic precedent retrieval using ML, NLP, and RAG. I delivered 91.5% classification accuracy and 95.2% prioritization precision (ensemble models: SVM, Naïve Bayes, Random Forest), improving legal data analysis efficiency by 40%+, and deployed cloud-native services with CI/CD, REST APIs, and vector similarity search.

Experience

Work history, roles, and key accomplishments

Current

AI Developer & Consultant

Current

University of Education Lahore

Jan 2026 - Present (5 months)

Architected and deployed an AI academic chatbot with a Smart RAG loop across 3 institutional knowledge bases, logging 100+ production queries and implementing GPT-4o-mini with Pinecone retrieval and query rewriting/chunk grading multi-retry retrieval. Built a React + Vite PWA with SSE streaming and a FastAPI backend integrating Pinecone, Hugging Face cross-encoder reranking, and Redis session-awar

Smart RAG Loop SSE PWA fastAPI Cross Encoder Reranking Cross Encoder Re Ranking

AI Engineer

Uraan AI Techathon 1.0

Sep 2025 - Oct 2025 (1 month)

Built a production-grade AI LegalTech SaaS platform with intelligent case classification and semantic precedent retrieval using ML/NLP RAG, achieving 91.5% classification accuracy and 95.2% prioritization precision and improving legal data analysis efficiency by 40%+. Developed a full-stack React + Streamlit application and deployed RESTful APIs with CI/CD on Render/Vercel using Hugging Face embed

NLP Ensemble Classification (SVM Naive Bayes Random Forest)Hugging Face Embeddings Llama 3.1 LLM RESTful APIs React Streamlit CI CD

Education

Degrees, certifications, and relevant coursework

University of Education, Lahore

Bachelor of Science, Computer Science

Grade: CGPA: 3.21/4.0

BS Computer Science student specializing in core CS and ML-related coursework, including Linear Algebra, Statistics, Calculus-I, OOP, DSA, Machine Learning, Data Science, and Database Systems.