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Software Engineer with 2+ years in AI/ML & Cloud Technologies
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Highly motivated Software Engineer pursuing a Bachelor of Science in Computer Science with a concentration in Artificial Intelligence. Possessing 2.5 years of experience across various roles including Research Assistant, Software Engineering Intern, and Machine Learning Intern. Proficient in Python, AI/ML frameworks (PyTorch, TensorFlow), cloud platforms (AWS), and diverse database and backend technologies. Proven ability to develop deep learning pipelines, implement AI/ML workflows, and build scalable data services, with a strong focus on innovative solutions and problem-solving.
FAST, National University of Computer and Emerging Sciences
Bachelor of Science · Computer Science (Concentration: Artificial Intelligence)
August 1, 2022 – June 30, 2026
Genesys Research Lab, FAST NUCES
Research Assistant (Part-Time)
September 1, 2025 – June 1, 2026
Islamabad, Islamabad Capital Territory, Pakistan
SKAI Worldwide
Remote Software Engineering Intern
April 1, 2025 – October 1, 2025
Seoul, Seoul, South Korea
FAST NUCES
Teaching Assistant & Lab Demonstrator
August 1, 2024 – May 1, 2025
Islamabad, Islamabad Capital Territory, Pakistan
Genesys Research Lab, FAST NUCES
Machine Learning Intern
June 1, 2024 – September 1, 2024
Islamabad, Islamabad Capital Territory, Pakistan
TennisVision - Final Year Project
June 1, 2026 – Present
Built an end-to-end deep learning pipeline to analyze single-camera tennis footage for player biomechanics benchmarking against professional references, covering player detection, pose estimation, ball tracking, hit event localization, and stroke classification. Employed YOLOv8 (fine-tuned for tennis court player detection), RTMPose-Large for 2D pose estimation with SmoothNet for temporal smoothing, and TrackNet with an inpainting network for ball trajectory tracking and occlusion recovery. Adapted F3ED (a feature extractor + GRU + MLP architecture from the F3Set benchmark) for tennis hit event detection from pose and ball trajectory features. Fine-tuned the Badminton Stroke-type Transformer (BST) – originally trained for badminton – on the F3Set tennis split (7 stroke classes) to perform stroke classification; addressed class imbalance of underrepresented strokes (volley, drop) via keypoint mirroring, Gaussian noise injection, and speed perturbation augmentations, improving accuracy from 88% on the raw imbalanced dataset to 93%. Achieved a per-shot inference time of ~9 seconds on an RTX 2060 across the full pipeline. Served the pipeline through a FastAPI and Celery backend with Redis and MongoDB, paired with a React frontend for real-time dashboards and synchronized video playback.
Financial Sentiment Analysis & Topic Modeling
June 1, 2026 – Present
Engineered an experimental NLP pipeline on financial text incorporating LDA topic modeling to cluster semantic themes. Evaluated FinBERT, zero-shot/few-shot local LLMs, and a custom FAISS-based RAG architecture using sentence embeddings to compare retrieval-enhanced reasoning and classification accuracy.
Vision Transformer (ViT) vs. CNN Architectures
June 1, 2026 – Present
Implemented a custom Vision Transformer featuring image patching, linear embeddings, positional encoding, and multi-head self-attention to evaluate attention-based modeling against standard CNN architectures (with batch normalization and dropout) on the CIFAR-10 dataset. Designed fully-connected ANN models and hybrid CNN+ANN feature extractors from scratch without pre-trained weights.
Neural Machine Translation (English-to-Urdu)
June 1, 2026 – Present
Built an NLP translation system using a vanilla RNN encoder-decoder architecture, handling word-level tokenization and sequence encoding with padding masks. Performed systematic grid search hyperparameter tuning and implemented greedy and beam search inference decoding.
Image Denoising & Variational Autoencoders
June 1, 2026 – Present
Designed a convolutional Denoising Autoencoder to reconstruct CIFAR-10 images from injected Gaussian and salt-and-pepper noise. Implemented a VAE on Fashion-MNIST using the reparameterization trick to sample latent vectors for synthetic image generation, and evaluated reconstruction and KL-divergence losses.
Real-Time Crime Analytics Pipeline
June 1, 2026 – Present
Built a real-time streaming data pipeline using Kafka and Storm, alongside PySpark for batch analytics and ETL formatting of time-series CSV data. Applied K-Means clustering to location data to identify crime hotspots, exporting metrics to PostgreSQL and MongoDB for a Streamlit visualization dashboard.
Predictive Modeling of California Housing Prices
June 1, 2026 – Present
Conducted exploratory data analysis and benchmarked Linear Regression, SGD, and Polynomial models using K-fold cross-validation. Natively implemented batch and stochastic gradient descent optimization algorithms in NumPy.
DevFlow – AI Requirements Assistant
June 1, 2026 – Present
Built an interactive requirements assistant with Python and LangGraph using Gemini LLMs, implementing state management with Pydantic and LangGraph's StateGraph for multi-step interactions. Automated DOCX requirements document generation with python-docx.
Online Event Booking Platform
June 1, 2026 – Present
Designed a scalable, polyglot microservices architecture using Python (Flask, FastAPI), Node.js, and Spring Boot with PostgreSQL and MongoDB. Automated deployment on an AWS Kubernetes cluster using a CI/CD pipeline with Docker, Terraform, and GitHub Actions.
MedBot - AI Medical Assistant
June 1, 2026 – Present
Developed an AI-driven medical assistant, initially built using a quantized Llama 3.1 8B model trained on a medical dataset, then transitioned to the Google Gemini 1.5 Flash API due to hardware limitations. Built the backend with Python, Flask, TensorFlow, and PyTorch, with a frontend in HTML, CSS, and JavaScript.
Simulation of IPFS (Decentralized Storage)
June 1, 2026 – Present
Simulated the InterPlanetary File System (IPFS) in C++ to study peer-to-peer distributed file storage, using a Distributed Hash Table (DHT) for routing and a B-Tree for storage optimization.
Multi-threaded Pac-Man
June 1, 2026 – Present
Built a multi-threaded game in C++ and SFML, implementing synchronization techniques using mutexes, semaphores, and Pthreads to manage UI, Pac-Man, and ghost AI on separate threads to avoid deadlocks.
Advanced Learning Algorithms
Stanford/Coursera
June 1, 2026 – Present
Supervised Machine Learning
Stanford/Coursera
June 1, 2026 – Present
Developing Back-End Apps with Node.js
IBM
June 1, 2026 – Present
Introduction to Cloud Computing
IBM
June 1, 2026 – Present
Git & GitHub
IBM
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse academic projects, involvement in research labs, and interest in various technical domains (AI, hardware-software integration, automotive mechanics) suggest a curious, driven, and adaptable individual. Their participation in programming competitions indicates a competitive spirit and a willingness to tackle challenging problems, which aligns well with a culture of innovation and continuous improvement. The academic focus, however, means their experience with corporate cultural norms and team dynamics is less evident.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving skills and an ability to work on complex, multi-faceted technical challenges. Their role as a Teaching Assistant suggests good communication and mentoring abilities. The academic nature of most projects, however, means direct operational fit in a fast-paced commercial environment needs further validation.