AI-focused Software Engineer building intelligent, scalable enterprise applications across web, mobile, and desktop platforms. Experienced in frontend architecture, AI integration, and production-grade system design.
Design and implementation of agentic AI systems using LangChain and LangGraph, including multi-agent coordination, RAG pipelines, and hybrid graph-vector retrieval.
Neo4j-based knowledge graph modeling, vector similarity search, retrieval ranking, and threshold-based routing with confidence control.
Reasoning trace layers, decision transparency, and human-in-the-loop workflows to improve reliability and system trust.
Enterprise-grade web and mobile architecture using Angular, React, and TypeScript with scalable UI systems for AI-driven applications.
API design, microservice integration, and containerized deployments supporting production-ready AI platforms.
Team leadership, architectural decision-making, and Agile delivery for enterprise systems.
Provide technical leadership across enterprise platforms within IBM Business Automation Workflow (BAW), progressing from Lead to Senior Lead Software Engineer. Contribute to World Check Next Gen, Data Curation, Name Matcher, Contributor Model, and Low Code initiatives. Lead architectural decisions, API integrations, Docker-based deployments, and frontend performance optimization. Serve as Scrum Master and project lead, coordinating technical delivery across teams and stakeholders.
Tech Stack: JavaScript, TypeScript, Java, Python, JSON, YAML, IBM Business Automation Workflow (BAW), Swagger OpenAPI, Docker, React
Led UI/UX and frontend architecture for Desktop, Mobile, and Web AI applications. Developed JavaFX applications and backend services using Spring Boot while supporting AI-integrated systems across MySQL and Tomcat environments.
Tech Stack: Java, Spring Framework, Spring Boot, JavaFX, GluonFX, MySQL, Weka, Tomcat, D3JS
Led frontend architecture for web and mobile applications using Angular and Ionic. Built modular UI systems within an Nx monorepo structure and integrated backend services using Node.js and GraphQL. Applied performance optimization strategies including Service Workers and Lighthouse.
Tech Stack: Angular CLI, RxJS, Ionic, TypeScript, Node.js, GraphQL, Apollo, Mongoose, Firebase, Tailwind, SCSS/LESS, Nx
Developed responsive web and mobile applications with a focus on UI/UX improvements. Built and maintained e-commerce systems using OpenCart and integrated backend services to support business operations.
Tech Stack: PHP, JavaScript, HTML, CSS, jQuery, Bootstrap, Ajax, OpenCart
Built responsive web and mobile interfaces and supported frontend optimization efforts. Contributed to UI/UX design and integrated PHP-based backend systems.
Developed consultant and e-commerce websites and contributed to digital marketing initiatives. Built a skin data tracking application.
Tech Stack: Web Technologies, E-commerce Platforms, Digital Marketing Tools
Built business models and technology strategies. Developed e-commerce platforms and provided SEO and SEM consulting.
Tech Stack: E-commerce Platforms, SEO/SEM Tools
Developed e-commerce websites and landing pages. Performed WordPress performance optimization and customization.
Tech Stack: WordPress, Magento, OpenCart, PHP, JavaScript, HTML, CSS
Designed logos, branding assets, and marketing materials for various businesses.
Tools: Photoshop, Illustrator, Adobe Creative Suite
Tech Stack: WordPress, Magento, OpenCart, PHP, JavaScript, HTML, CSS
GPA: 3.86
Track: Software Engineering
Track: Software Engineering
Analyzed and compared Effort-Size (story points) and Effort-Time (ideal time) expert-based estimation methods in software engineering. Evaluated linear regression fit and scalar variability effects to assess correlation with actual effort.
Findings showed both methods positively correlate with completion effort, with Effort-Time demonstrating stronger regression fit in the sample set. Results highlight variability in human estimation and reinforce that no single estimation method should be treated as a universal solution.
Proposed “Human-Story” as a structured writing technique to improve requirement quality in Agile software development. Compared User-Story, Persona-Story, and Human-Story using the INVEST Grid and Agile requirement checklists.
Results demonstrated higher completeness and clarity in Human-Story (84%) compared to User-Story (44%) and Persona-Story (56%), supporting improved requirement specification practices.
Credential ID: 81801535
Credential ID: 79486521
Credential ID: 79486521
OpenQuery transforms enterprise documents like RFPs, contracts, policies, spreadsheets, and support logs into an AI-ready knowledge engine that answers questions with context and citations. It handles the full ingestion pipeline — extraction, classification, grouping, embedding, and semantic search — so teams get accurate, traceable answers instead of digging through scattered files.
The system supports integrations such as LINE messaging and webhook channels, and uses a unified token system to manage usage across extraction, embeddings, and AI queries.
Core concepts: document ingestion and extraction, semantic vector search, hybrid retrieval, classification and grouping, AI-based reasoning with source citations.
Kent Wyn n AI provides secure, OpenAI-compatible AI endpoints for chat, completions, embeddings, and tool calling, all hosted on Kent Wynn’s infrastructure. Developers generate scoped API tokens and build intelligent features with predictable latency and transparent usage monitoring.
The platform includes reasoning and embedding models that integrate seamlessly with common SDKs and frameworks, enabling private, high-performance AI services for production applications.
Core concepts: private AI infrastructure, OpenAI-compatible REST APIs, reasoning and embedding models, token management, low-latency inference, developer SDK support.
Contributed to the development of ClarioEx, a customizable hybrid AI engine designed to support multiple machine learning algorithms within a single system. The platform enables dynamic selection and combination of over 10 algorithms to improve model robustness and adaptability across varying data conditions.
Supports multi-algorithm execution to evaluate and identify optimal outputs based on dataset characteristics rather than relying on a single predefined approach.
Engineer focused on building production-grade AI systems and scalable enterprise applications across web, mobile, and desktop platforms.
Experienced in agentic AI architecture, hybrid retrieval pipelines, and knowledge graph integration, with strong foundations in frontend architecture, Java-based systems, and API-driven application design.
Combines intelligent system orchestration with enterprise software engineering practices to deliver reliable, high-performance solutions.
