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AI Engineer with less than a year in LLM Pipelines & NLP with academic project experience.
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I am a Computer Science student from FAST NUCES Peshawar specializing in LLM pipelines, NLP, and Al automation systems. Built an end-to-end intelligent query routing system as FYP involving LLM orchestration, structured output generation, and full-stack deployment. Seeking a role in Al/ML or workflow automation.
FAST NUCES, Peshawar
BS Computer Science · Computer Science
N/A – Present
Jinnah College for Women, University of Peshawar
Intermediate
N/A – Present
Agricultural University Public School, Peshawar
Matriculation
N/A – Present
Tazafa - Lightweight Adaptive LLM Routing System (FYP)
January 1, 2026 – Present
Designed an end-to-end LLM orchestration pipeline that analyzes user queries, extracts domain and complexity structure, and routes them to the most suitable locally-hosted model using a Random Forest classifier trained on 386-dimensional sentence embeddings. Built an LLM-as-judge evaluation framework to automatically score model outputs across four Ollama-hosted models (gemma2:2b, phi4-mini, qwen1.5:0.5b, qwen2.5-coder:0.5b) and generate structured training labels from unstructured text. Implemented a composite scoring function balancing response quality against runtime resource cost (latency, CPU, RAM), achieving ~88% weighted F1 on held-out data. Deployed a full-stack system with a FastAPI backend, React frontend, and Supabase database on consumer hardware demonstrating viable local AI inference without cloud APIs.
Career Prediction System
January 1, 2026 – Present
Trained and benchmarked multiple ML classifiers to recommend career paths based on academic profiles, achieving 89% test accuracy. Evaluated model performance using precision, recall, and F1-score across multiple career categories.
Business Intelligence Dashboards
January 1, 2026 – Present
Built interactive HR Analytics and Sales dashboards on 1,400+ employee records, visualizing KPIs including attrition, salary distribution, and departmental sales trends.
Tour and Travel Management System
January 1, 2026 – Present
Developed a full-stack website for managing tour listings, bookings, and customer records with a MySQL backend.
Hospital Management System
January 1, 2026 – Present
Built a console-based system supporting patient appointment booking, doctor selection, and payment processing using object-oriented principles across 5+ modules.
Clinic Simulation System
January 1, 2026 – Present
Simulated a multi-threaded clinic environment with concurrent patient, receptionist, nurse, and doctor threads using pthreads and POSIX semaphores. Handled race conditions across up to 30 patients and 3 doctor-nurse pairs using mutexes, ensuring thread-safe access to all shared queues.
Google Carbon Programming
Udemy
June 1, 2026 – Present
Learn Microservices with Docker
Udemy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio, particularly the FYP on LLM routing, aligns well with an AI Engineer role, showcasing a keen interest and practical experience in cutting-edge AI/ML domains. The diversity of academic projects, ranging from full-stack web development to system simulations and business intelligence, indicates a broad technical curiosity and adaptability. The focus on local AI inference and resource optimization in the FYP also suggests an innovative and resource-conscious mindset. However, the lack of collaborative projects or team-based experience in the resume makes it difficult to fully assess cultural fit regarding teamwork and interpersonal skills.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations like the LLM routing system and clinic simulation. The ability to work on end-to-end systems, from model training and evaluation to full-stack deployment, indicates a proactive and holistic approach to project development. The academic projects suggest a capacity for independent learning and application of theoretical concepts to practical scenarios. However, without professional experience, the operational fit in a fast-paced industry environment is yet to be fully validated.