I am a Data Scientist specializing in building and deploying end-to-end AI systems that solve real-world business and operational challenges.

My experience spans the complete machine learning lifecycle—from data engineering and model development to large-scale deployment, monitoring, and optimization in production environments.

• Machine Learning & Deep Learning

• Computer Vision & Image Understanding

• Object Detection & Image Segmentation

• Generative AI & Large Language Models

• Statistical Modeling & Predictive Analytics

• MLOps & Model Deployment

• Cloud-Native AI Systems

• Geospatial AI & Remote Sensing

Platform URL: https://unnati.arms4ai.com/

Enterprise-grade geospatial intelligence platform that combines satellite imagery, machine learning, and Large Language Models (LLMs) to deliver actionable insights through natural language interactions. Designed for environmental monitoring, infrastructure assessment, land-use analytics, and decision support at scale.

Technologies: LLMs, Computer Vision, Geospatial AI, AWS, Flask, Docker, MLOps

Platform URL: https://thetreehealthproject.netlify.app/

AI-powered computer vision platform for automated tree detection, species classification, canopy health assessment, biomass estimation, carbon stock analysis, and biodiversity monitoring using satellite imagery and deep learning models.

Technologies: Computer Vision, Deep Learning, Segmentation Models, Foundation Models, Geospatial Analytics

Platform URL: https://agri.arms4ai.com/

Production-scale agricultural analytics platform enabling crop monitoring, field boundary extraction, vegetation health assessment, yield estimation, water stress detection, and precision agriculture workflows using machine learning and Earth Observation data.

Technologies: Machine Learning, Remote Sensing, Time-Series Analytics, Deep Learning, MLOps

Platform URL: https://road.arms4ai.com/

Computer vision and geospatial analytics platform for automated road extraction, road quality assessment, traffic monitoring, infrastructure inventory management, and change detection using state-of-the-art segmentation and object detection models.

Technologies: Object Detection, Semantic Segmentation, Deep Learning, Computer Vision, Geospatial AI

Built an LLM-powered geospatial analytics platform enabling users to perform complex GIS and Earth Observation workflows using natural language queries, reducing analysis turnaround time from months to minutes.

Tech Stack: LLMs, LangChain, Vector Search, Geospatial AI, AWS

Developed production-grade object detection pipelines using YOLO and RetinaNet for large-scale excavation site identification and monitoring from satellite imagery.

Tech Stack: YOLOv5, RetinaNet, PyTorch, MLflow, Docker, AWS

Built automated road extraction pipelines leveraging U-Net, DeepLabV3+, Segment Anything Model (SAM), and transformer-based architectures for large-scale infrastructure mapping.

Tech Stack: U-Net, DeepLabV3+, SAM, PyTorch, Computer Vision

Developed end-to-end deep learning pipelines for tree extraction, species classification, health estimation, biomass calculation, and carbon stock analytics using multispectral satellite imagery.

Tech Stack: Computer Vision, Deep Learning, Geospatial Analytics, Foundation Models

Designed AI systems for road quality assessment using SAR imagery, computer vision, and machine learning models to automate infrastructure monitoring at scale.

Tech Stack: SAR Imagery, Deep Learning, PyTorch, AWS

Developed machine learning solutions for crop monitoring, field boundary extraction, yield estimation, water productivity assessment, and sustainability reporting.

Tech Stack: Remote Sensing, Deep Learning, Machine Learning, Computer Vision

• Machine Learning

• Deep Learning

• Computer Vision

• Generative AI

• Large Language Models (LLMs)

• Statistical Analysis

• Predictive Modeling

• Feature Engineering

• PyTorch

• TensorFlow

• Scikit-Learn

• OpenCV

• Hugging Face

• LangChain

• MLflow

• Object Detection (YOLO, RetinaNet, Faster R-CNN)

• Semantic Segmentation (U-Net, DeepLabV3+, SAM)

• Instance Segmentation (Mask R-CNN)

• Vision Transformers (ViT)

• Foundation Models

• Python

• SQL

• Pandas

• NumPy

• Matplotlib

• Seaborn

• AWS

• Docker

• GitHub Actions

• CI/CD

• Linux

• Git

• Model Monitoring

• Production Deployment

• QGIS

• GDAL

• Rasterio

• GeoPandas

• Google Earth Engine

• Remote Sensing

• Foundation Models

• Generative AI Systems

• Computer Vision at Scale

• Multimodal AI

• Agentic AI Systems

• Retrieval-Augmented Generation (RAG)

• Large-Scale Machine Learning

• AI Infrastructure & MLOps

• Earth Observation & Geospatial Intelligence

📧 Email: neoalwayz@gmail.com

💼 LinkedIn: https://linkedin.com/in/neoalwayz