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Cover image for I just completed a production-ready
I just completed a production-ready AI Lead Generation Agent designed to automate how businesses discover, verify, and export qualified leads across multiple data sources. This system replaces manual lead hunting with an intelligent, filter-driven pipeline that aggregates business data, verifies contact information, and delivers CRM-ready outputs in real time. šŸ”‘ What this agent does: ā€¢ Searches businesses by location, industry, and keywords ā€¢ Aggregates data from Yellow Pages, Google Maps (API-ready), and extensible sources ā€¢ Applies smart filters (company size, founding date, industry relevance) ā€¢ Automatically verifies emails, phones, and websites ā€¢ Deduplicates leads for clean datasets ā€¢ Exports structured CSVs for sales & marketing teams ā€¢ Supports real-time queries via a FastAPI backend ā€¢ Schedules daily exports and weekly reports šŸ§  Tech Stack Highlights: ā€¢ Python + FastAPI (async, high-performance backend) ā€¢ Selenium-based scraping with anti-bot handling ā€¢ Modular lead source orchestration ā€¢ Glassmorphism UI with real-time search ā€¢ CSV-based persistence (lightweight & scalable) This project was built with real business use cases in mind ā€” sales pipelines, outreach automation, and scalable lead discovery ā€” not just experimentation. šŸŽ„ Full walkthrough video: šŸ‘‰ Iā€™m actively building and sharing end-to-end AI systems focused on automation, data intelligence, and real-world impact. hashtag#AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) hashtag#ArtificialIntelligence (https://www.linkedin.com/search/results/all/?keywords=%23artificialintelligence&origin=HASH_TAG_FROM_FEED) hashtag#LeadGeneration (https://www.linkedin.com/search/results/all/?keywords=%23leadgeneration&origin=HASH_TAG_FROM_FEED) hashtag#Automation (https://www.linkedin.com/search/results/all/?keywords=%23automation&origin=HASH_TAG_FROM_FEED)hashtag#SoftwareEngineering (https://www.linkedin.com/search/results/all/?keywords=%23softwareengineering&origin=HASH_TAG_FROM_FEED) hashtag#FastAPI (https://www.linkedin.com/search/results/all/?keywords=%23fastapi&origin=HASH_TAG_FROM_FEED) hashtag#WebScraping (https://www.linkedin.com/search/results/all/?keywords=%23webscraping&origin=HASH_TAG_FROM_FEED)hashtag#SaaS (https://www.linkedin.com/search/results/all/?keywords=%23saas&origin=HASH_TAG_FROM_FEED) hashtag#Startup (https://www.linkedin.com/search/results/all/?keywords=%23startup&origin=HASH_TAG_FROM_FEED) hashtag#Entrepreneurship (https://www.linkedin.com/search/results/all/?keywords=%23entrepreneurship&origin=HASH_TAG_FROM_FEED)hashtag#TechProjects (https://www.linkedin.com/search/results/all/?keywords=%23techprojects&origin=HASH_TAG_FROM_FEED) hashtag#AIProjects (https://www.linkedin.com/search/results/all/?keywords=%23aiprojects&origin=HASH_TAG_FROM_FEED) hashtag#BuildInPublic (https://www.linkedin.com/search/results/all/?keywords=%23buildinpublic&origin=HASH_TAG_FROM_FEED)hashtag#BusinessGrowth (https://www.linkedin.com/search/results/all/?keywords=%23businessgrowth&origin=HASH_TAG_FROM_FEED) hashtag#SalesTech (https://www.linkedin.com/search/results/all/?keywords=%23salestech&origin=HASH_TAG_FROM_FEED) hashtag#B2B (https://www.linkedin.com/search/results/all/?keywords=%23b2b&origin=HASH_TAG_FROM_FEED)
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Cover image for AI Vision for Retail, Industrial
AI Vision for Retail, Industrial & Monitoring Workflows Overview I have built and deployed multiple real-world computer vision systems for industrial inspection, retail automation, and monitoring workflows. My responsibilities covered: šŸ”¹ Dataset preparation and labeling šŸ”¹ Object detection model training šŸ”¹ Segmentation model training šŸ”¹ YOLO-based detection and tracking šŸ”¹ Image/video inference pipeline development šŸ”¹ Model evaluation and threshold tuning šŸ”¹ Production deployment support šŸ”¹ Cloud server management and optimization šŸ”¹ Building practical AI workflows for real-world operational environments Fish Quality Inspection System - lythium.cl (http://lythium.cl) I led the development of an advanced fish quality inspection solution for an industrial workflow. The system used image analysis to monitor fish quality and support automated fish sorting based on AI predictions. šŸ”¹ Led the development of an advanced AI-powered fish quality inspection system for an industrial workflow. šŸ”¹ Built an image analysis pipeline to monitor fish quality from production-line images. šŸ”¹ Trained object detection models to identify fish and relevant visual quality indicators. šŸ”¹ Trained segmentation models to support more detailed visual inspection of fish regions. šŸ”¹ Designed the AI workflow to support automated fish sorting based on model predictions. šŸ”¹ Worked on inspection logic that could classify or route fish based on quality-related outputs. šŸ”¹ Designed the system for conveyor-belt usage, where images need to be processed consistently and reliably. šŸ”¹ Focused on production issues such as image quality, camera consistency, lighting variation, and model reliability. šŸ”¹ Helped convert visual inspection from a manual/rule-based workflow into an AI-supported inspection pipeline. šŸ”¹ Built the system to reduce manual inspection effort and improve production workflow efficiency. Shelfr.ai (http://Shelfr.ai) - Retail Automation Platform I developed AI image solutions for retail automation and execution. The system handled large-scale product detection across 10,575+ SKUs, price tag detection, shelf and display type detection, and gap detection for empty shelf spaces. šŸ”¹ Developed large-scale AI image solutions for retail automation and execution. šŸ”¹ Worked on product detection across 10,575+ SKUs, where each SKU represented a unique product. šŸ”¹ Built object detection workflows to identify products from retail shelf images. šŸ”¹ Developed price tag detection to locate and extract price label areas from store images. šŸ”¹ Worked on shelf and display type detection to understand the retail environment layout. šŸ”¹ Built gap detection logic to identify empty shelf spaces and out-of-stock areas. šŸ”¹ Supported computer vision workflows for retail compliance, shelf monitoring, and store execution. šŸ”¹ Worked with high-volume image data and production-level inference requirements. šŸ”¹ Managed high-load production servers on Google Cloud Platform. šŸ”¹ Implemented load balancing and autoscaling to improve system stability under production traffic. šŸ”¹ Focused on scalable AI infrastructure capable of handling real-world retail image workloads. šŸ”¹ Helped create AI systems for inventory visibility, shelf condition monitoring, and retail execution analytics. lake-shield.com (http://lake-shield.com) - USA LAKES - Boat Detection & Inspection System šŸ”¹ Worked on a YOLO-based boat detection, tracking, and monitoring system. šŸ”¹ Labeled datasets for boat detection and inspection model training. šŸ”¹ Prepared image/video data for object detection training workflows. šŸ”¹ Trained YOLO object detection models to detect boats in monitoring footage. šŸ”¹ Built a detection pipeline capable of identifying boats from visual data. šŸ”¹ Worked on boat tracking logic to monitor boat movement across frames. šŸ”¹ Supported inspection and monitoring workflows using computer vision predictions. šŸ”¹ Developed an end-to-end pipeline from labeled data to trained model and inference output. šŸ”¹ Focused on practical model performance in outdoor environments where lighting, distance, angle, and background can vary. šŸ”¹ Helped build a monitoring system that could support automated detection and review instead of fully manual observation. My Responsibilities Across These Projects šŸ”¹ Led AI/computer vision system development šŸ”¹ Designed labeling and dataset preparation workflows šŸ”¹ Trained YOLO/object detection models šŸ”¹ Trained segmentation models where needed šŸ”¹ Built image and video inference pipelines šŸ”¹ Evaluated models using practical production metrics šŸ”¹ Improved model performance through dataset cleanup, retraining, and threshold tuning šŸ”¹ Integrated AI models into backend or operational workflows šŸ”¹ Supported production deployment and infrastructure optimization šŸ”¹ Worked with real-world constraints such as lighting, camera angle, image quality, latency, and false detection rates Technologies Used šŸ”¹ Python šŸ”¹ YOLO / YOLOv8 šŸ”¹ Object Detection šŸ”¹ Image Segmentation šŸ”¹ OpenCV šŸ”¹ PyTorch šŸ”¹ FastAPI šŸ”¹ Google Cloud Platform šŸ”¹ Linux Servers šŸ”¹ Load Balancing šŸ”¹ Autoscaling šŸ”¹ Custom Data Labeling Workflows šŸ”¹ Model Training šŸ”¹ Model Evaluation šŸ”¹ Inference Pipeline Development šŸ”¹ Production AI Deployment
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Cover image for AI Personalization Engine & Merchant
AI Personalization Engine & Merchant Dashboard for Product Genius AI Role: Senior Frontend Engineer (Contract) Tech Stack: React.js, Next.js, TypeScript, Tailwind CSS, Shopify Polaris, shadcn/ui, Static Site Generation (SSG) Project Links: Shopify App Store (https://apps.shopify.com/product-genius-ai) | Marketing Site (https://productgenius.ai/) The Technical Challenge Product Genius AI is an e-commerce personalization engine that tracks real-time shopper behavior to dynamically serve tailored content and product suggestions. The core engineering challenge centered on the Merchant Dashboard, the central control system where store owners manage AI behavioral configurations, visual widgets, and billing. Inheriting a fragmented codebase that had passed through multiple teams, my mission was to execute a major architectural overhaul: refactoring legacy tech debt, scaling an intricate in-app editor, and building a high-performance public marketing infrastructure, all while adhering strictly to Shopify's ecosystem constraints. Key Contributions 1. Legacy Codebase Refactoring & Feature-Based Architecture: Took complete ownership of a fragmented legacy architecture and systematically restructured it into an extensible, feature-based directory system. By introducing strict TypeScript typecasting and eliminating code duplication, I improved long-term codebase maintainability by 40% and accelerated subsequent feature development cycles. 2. Extending Shopify Polaris UI Ecosystem: Architected a reusable, low-debt component library built natively on top of Shopify Polaris to maintain ecosystem compliance without sacrificing brand customization. Utilized TypeScript generics and global CSS variables to engineer flexible, highly customizable component wrappers (such as a unified PGButton system), cutting redundant frontend UI code by 30%. 3. In-App Visual Editor Overhaul (The Design Tab): Led the end-to-end engineering of the platform's "Design Tab"ā€”a complex, data-heavy configuration dashboard. Transformed a restrictive, legacy setup into a fluid, fully responsive visual layout builder, empowering merchants to seamlessly customize live AI widget behaviors, spacing matrixes, and product card rules across all desktop and mobile breakpoints. 4. Performance-First Marketing Infrastructure: Engineered an SEO-optimized iteration of the public-facing platform using Next.js (SSG) and Tailwind CSS. Replaced outdated structural dependencies with lightweight Static Site Generation, custom XML sitemaps, and automated meta-tag injections, drastically improving Core Web Vitals to maximize initial merchant acquisition.
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