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AI Engineer with less than a year in Python-based RAG Workflows & LLM Applications.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with production experience building Python-based data collection, structured extraction, classification, and RAG workflows. Hands-on background across LLM applications, retrieval systems, AI automation, backend APIs, computer vision, MLOps, and Dockerized services. M.Sc. Robotics & AI candidate with a B.Sc. in Artificial Intelligence.
National University of Sciences and Technology (NUST)
M.Sc. Robotics & Artificial Intelligence · Robotics & Artificial Intelligence
August 1, 2025 – Present
FAST - National University of Computer & Emerging Sciences
B.Sc. Artificial Intelligence · Artificial Intelligence
August 1, 2021 – June 30, 2025
Raqim International
AI Engineer
June 1, 2025 – March 1, 2026
Sialkot, Punjab, Pakistan
National Aerospace Science & Technology Park (NASTP)
Intern
June 1, 2024 – August 1, 2024
Islamabad, Islamabad Capital Territory, Pakistan
LiDAR-Based Road Surface Quality Index
June 27, 2026 – Present
Engineered a LiDAR point-cloud ML pipeline for International Roughness Index prediction with PCA-based features and Google Maps visualization for roadway monitoring.
Hybrid Vision Architecture with Evolutionary Weight Merging
June 27, 2026 – Present
Improved CIFAR-10 corruption robustness by up to 3.45% on Gaussian noise and 2.39% on frost using evolutionary checkpoint merging and Transformer-based refinement.
WhatsApp Personal Assistant - Personal RAG System
June 27, 2026 – Present
Built a personal knowledge assistant for 6 users with hybrid SQLite FTS5 and ChromaDB retrieval, Gemini-powered search, AES-256 storage, and WhatsApp Business integration with sub 1.5s response times.
Story2Audio - Emotion-Aware Speech Synthesis
June 27, 2026 – Present
Designed a Dockerized speech synthesis system supporting 7 emotions using RoBERTa and Bark TTS, achieving ~510 ms median inference latency under load testing.
Cultural Fit Analysis
The candidate's project portfolio shows a blend of academic rigor and practical application, indicating a strong drive for innovation and continuous learning. The personal projects demonstrate initiative and passion beyond formal employment. The experience in building end-to-end systems suggests a preference for ownership and delivering tangible results. The breadth of technologies and problem domains tackled indicates a versatile and curious mindset, which would likely fit well into a dynamic AI engineering team.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving skills (e.g., reducing lead discovery time, improving corruption robustness) and an ability to deliver performant systems (sub 1.5s response times, ~510 ms inference latency). The diverse range of projects, from personal assistants to industrial applications, suggests adaptability and a proactive approach to learning and application. The experience at Raqim International demonstrates an understanding of business impact and automation.