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AI Engineer with 1+ years in AI/MLOps and Full Stack Development.
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Faheela Shafi is a highly skilled AI & Automation Engineer with expertise in Python, C++, JavaScript, and frameworks like MERN and PyTorch. She has a strong background in Computer Vision, MediaPipe, and cloud platforms including AWS and Kubernetes. Her project experience includes developing real-time transport systems, hand gesture drawing applications with computer vision, language models, and head-detection systems. With a solid foundation in both AI/ML and full-stack development, she is adept at building scalable and intelligent solutions.
FAST-NUCES
BS · Computer Science
August 1, 2022 – Present
Microslush
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SmartU Transport: Smart University Transport Management System
January 1, 2022 – Present
Built a real-time university transport platform using Flutter, FastAPI, PostgreSQL, and WebSockets; delivered GPS-powered shuttle tracking, live seat availability, RFID attendance automation, digital seat booking, and event-driven notification services. Developed a role-based transport operations dashboard supporting route management, fee and fine administration, occupancy analytics, transport usage reporting, and automated student communications for day scholars and hostelite commuters.
Hand Gesture Drawing App — Real-Time Computer Vision Drawing Interface
January 1, 2022 – Present
Built an in-browser drawing application using MediaPipe Hands and HTML Canvas, enabling real-time hand tracking with gesture-based interactions. Implemented pinch-to-draw, peace-sign color switching, index-finger erasing, and open-palm canvas reset; optimized gesture recognition for smooth, low-latency user experience.
Mini GPT Language Model — Character-Level Transformer (PyTorch)
January 1, 2022 – Present
Built a GPT-style autoregressive language model from scratch, implementing tokenization, self-attention, training loops, and text generation in PyTorch. Trained on curated Friends dialogue data to learn conversational patterns and generate context-aware character-level text sequences.
CrowdGuard — YOLOv8 Head-Detection & Overcrowding Alert
January 1, 2022 – Present
Fine-tuned YOLOv8 on JHU Crowd++ with FP16 training and INT8 quantization; Vercel-hosted dashboard with real-time threshold alerts for high-density environments.
Web Scraping Automation — Business Intelligence Data Extraction
January 1, 2022 – Present
Built Selenium-based automation pipelines to extract business listings from Google Maps; handled infinite scrolling, dynamic DOM rendering, stale elements, rate limiting, and anti-bot challenges for reliable large-scale data collection. Implemented automated data cleaning, validation, and structured export workflows for downstream analytics and business intelligence applications.
Spotify Listening Behavior Analysis — Data Analytics & Visualization
January 1, 2022 – Present
Performed exploratory data analysis (EDA) on Spotify listening datasets using Pandas, NumPy, and Matplotlib; analyzed user listening patterns, artist preferences, track popularity, and temporal consumption trends. Developed data visualizations and statistical summaries to uncover behavioral insights and support data-driven decision-making.
Masked Autoencoder (MAE) — Self-Supervised Vision Pre-training (PyTorch)
January 1, 2022 – Present
Built from scratch: 12-layer ViT encoder + 12-layer Transformer decoder trained on Tiny ImageNet with 75% patch masking, mixed-precision, and cosine LR scheduling; achieved PSNR 26.56 dB / SSIM 0.7277 and deployed on Hugging Face Spaces.
CaptionGen — Vision-to-Language (NLP + PyTorch)
January 1, 2022 – Present
End-to-end LSTM image-captioning pipeline on Flickr: tokenization, vocabulary engineering, feature extraction, and optimized batching in PyTorch.
Cultural Fit Analysis
The candidate's project portfolio is diverse, covering computer vision, NLP, data analytics, and full-stack development, indicating a broad interest and willingness to explore different technical domains. The academic and personal projects align well with an AI Engineer role, showcasing initiative and self-driven learning. The freelance experience also suggests an ability to deliver solutions independently. However, the professional experience is relatively short-term, which might indicate a need for more sustained team collaboration experience in a structured environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations (e.g., handling infinite scrolling in web scraping, optimizing gesture recognition). The project diversity suggests adaptability and a proactive learning attitude. Experience in building end-to-end systems indicates an understanding of operational requirements, though specific soft skills like teamwork or leadership are not explicitly detailed in the provided data.