Resume

• Built 5+ production ML models reducing processing time by 40%
• Developed multi-agent systems using LangChain and AutoGen with 92% task completion accuracy
• Implemented automated workflows with Model Context Protocol, saving 15 hours/week
• Collaborated in Agile methodology with cross-functional teams for iterative development

• Distinguished academic record in AI Engineering and Deep Learning
• Excellence in AI Engineering with focus on Computer Vision

• Quantitative coursework in remote sensing, spatial statistics, and Python-based satellite-data modeling
• Outstanding performance in GIS and Remote Sensing

QLoRA fine-tuning of Qwen 2.5 7B Instruct for natural Urdu, Roman Urdu, and Urdu/English code-mixed chat, with RAG-aware retrieval over Urdu Wikipedia

• Fine-tuned Qwen 2.5 7B with QLoRA (154 MB adapter, 0.82% of parameters, 8.56 GB peak VRAM) across three versions, built end-to-end for under $60
• Achieved 79.5% pairwise win rate over the base model across two independent LLM judges on a 100-prompt evaluation set, recovering every regression from the prior version
• Ran a data-centric iteration loop that lifted code-mixed task win rate from 0% to 80% and made retrieval deployable, cutting the base model's Chinese-fallback on Urdu context from 45/100 to 0

Technologies: Python, PyTorch, Unsloth, QLoRA / PEFT, Hugging Face, Modal, Gradio, Haystack, Qdrant

ML system predicting PERM labor certification outcomes with SHAP explainability

• Built threshold-tuned Gradient Boosting classifier achieving 73.2% accuracy with 61% denied recall on 25K PERM records
• Implemented SHAP TreeExplainer with feature mapping to provide per-prediction explanations across 10 input features
• Designed 5-stage modular MLOps pipeline (ingestion, validation, transformation, training, evaluation) with model promotion gating

Technologies: Python, Scikit-learn, XGBoost, LightGBM, CatBoost, SHAP, FastAPI, Docker

Deep learning system for facial expression recognition in group photos using ensemble models

• Built emotion classification model achieving 80% accuracy on RAF-DB dataset across 7 emotion classes
• Implemented ensemble learning combining ResNet-18 and EfficientNet-B2 to handle severe class imbalance
• Deployed Flask web application with MTCNN face detection on Hugging Face Spaces using Docker

Technologies: PyTorch, Flask, MTCNN, ResNet-18, OpenCV, Docker, Hugging Face

ML regression model for agricultural yield forecasting using satellite imagery and climate data

• Built supervised ML model achieving 0.137 t/ha prediction error on test set
• Engineered features from multi-spectral satellite imagery and climate variables using Google Earth Engine
• Applied cross-validation and hyperparameter tuning for optimal model performance

Technologies: Scikit-learn, Python, NumPy, Pandas, Google Earth Engine, Feature Engineering

Comprehensive teacher rating and review platform for educational institutions

• Built full-stack web application with REST APIs for real-time data synchronization
• Implemented responsive design delivering seamless experience across all devices
• Achieved 60% bundle size reduction through code splitting and lazy loading optimizations

Technologies: React, TypeScript, Supabase, TanStack Query, Tailwind CSS, Vite

Geospatial AI system analyzing 22 years of satellite data for groundwater prediction (2002-2024)

• Developed ML regression models achieving R²=0.89 for groundwater level predictions across 145 districts
• Deployed Flask REST API serving ML models with real-time GRACE satellite data processing
• Engineered feature extraction pipeline processing 22 years of geospatial time-series data

Technologies: Scikit-learn, Flask, Google Earth Engine, React, Predictive Analytics, GRACE/GLDAS

ML-driven spatial optimization for Electric Vehicle infrastructure planning

• Implemented weighted scoring algorithm processing demographic, economic, and infrastructure layers for 5 tehsils
• Applied geospatial ML techniques for optimal site selection achieving 90%+ coverage target
• Integrated multi-criteria decision analysis with spatial data processing pipeline

Technologies: Python, Scikit-learn, QGIS, ArcGIS, OpenStreetMap, Multi-criteria Analysis