Deven – Python, AI agent development, OpenAI

A technology-focused project specializing in cybersecurity, fraud prevention, and Agentic AI solutions. The platform helps organizations strengthen security, mitigate fraud risks, and leverage advanced AI-driven automation to improve operational efficiency and decision-making.

• Developed a restaurant order hub and loyalty management system using Next.js, Supabase, and Twilio;
• Built an AI-powered grant research tool using Agno and Playwright, deployed on Linode;
• Created a health education streaming platform using Next.js, PostgreSQL, and Mux;
• Led modernization of a music streaming stack to reduce token usage, improve AI development speed, and streamline human agent review processes.

A healthcare institution that is part of a global network of approximately 600 non-profit hospitals, clinics, and dispensaries worldwide, providing a wide range of medical services and patient care with a focus on community health, preventive medicine, and accessible treatment.

• Built anomaly detection and centralized EMR analytics with Python and Dagster;
• Digitized data workflows and delivered Accounting pricing insights with Power BI and SQL Server;
• Developed EMR migration shell scripts for Finance and IT, speeding legacy-to-new data population.

A technology company specializing in operating system technologies, solutions, and services, with expertise across 5G, mobile technologies, AIoT, automotive systems, and enterprise software solutions. It focuses on delivering end-to-end software engineering capabilities, enabling digital transformation across connected devices and intelligent systems, and supporting scalable solutions for both consumer and enterprise markets.

• Optimized AI runtime performance on Intel CPUs and GPUs using Python and Intel OpenVINO;
• Profiled and improved inference pipelines to reduce latency and increase throughput in production environments;
• Integrated model optimization techniques such as quantization and graph-level optimizations to improve efficiency;
• Collaborated on deploying and validating AI workloads across heterogeneous hardware configurations;
• Debugged and resolved performance bottlenecks in AI inference workflows to ensure stable production-grade execution.

A technology company focused on building an AI ecosystem that enables organizations to develop and deploy their own AI applications, providing the tools and infrastructure needed to innovate and scale machine learning solutions across industries.

• Led the development of a Java Vert.x-based data versioning system for image and video labeling workflows, improving traceability and dataset management;
• Designed and implemented backend services to support scalable data annotation pipelines for large-scale machine learning datasets;
• Managed on-premise and cloud deployments using Docker, ensuring consistent and reliable environment orchestration across infrastructure;
• Improved system reliability and maintainability by optimizing service architecture and deployment workflows;
• Collaborated with cross-functional teams to support production-grade data processing and labeling infrastructure.