Led development of a criminal intelligence platform, integrating ODRC and CPD systems for seamless cross-database queries across 8+ law enforcement databases with 99.99% data consistency.Key Achievements:* Architected a scalable microservices infrastructure using Angular and C++, achieving 250K+ TPS.* Engineered a hybrid caching architecture with Redis and DynamoDB, reducing latency by 90% and supporting 15K+ concurrent users, while maintaining CJIS compliance.* Implemented a custom-trained Llama 3.2 LLM for criminal intelligence, achieving 98.5% prediction accuracy and enabling real-time pattern detection across 5M+ records.* Designed and deployed enterprise-grade Kubernetes orchestration with cross-region failover, maintaining 99.999% uptime.* Developed a military-grade security framework, exceeding CJIS and Ohio state security requirements with zero breaches.* Deployed robust ETL pipelines using Python, ensuring data quality and standardization across multiple sources.* Implemented RAG using Llama 3.2 for intelligent document processing, automating extraction from unstructured data with 95% accuracy.* Reduced technical debt by 85% and maintained zero critical vulnerabilities through SonarQube and automated testing.Led cross-functional teams, established data processing standards, and orchestrated cloud infrastructure using AWS services.

Collaborated with a team of three developers to develop "RAF Vue," a scalable auditing application for United Audit Systems Inc. (UASI), leveraging JavaScript, Node.js, Express.js, MySQL, HTML5, CSS, and jQuery.Key Achievements:* Developed a scalable auditing application using MVC architecture and RESTful APIs, resulting in a 30% increase in auditing efficiency. * Implemented intuitive user interfaces using React.js, jQuery, and JavaScript, with HTML5 and CSS, leading to a 25% improvement in user satisfaction.* Built a robust backend using Node.js, Express.js, and MySQL, optimizing performance and scalability to manage a 50% increase in data volume.* Reduced development time by 20% through Agile (Scrum) and Git collaboration.* Transformed UASI's insurance claim validation process, boosting efficiency by 40% and reducing errors by 60% through the auditing application.* Delivered a high-quality solution that surpassed expectations, leveraging React.js and MySQL to reduce manual effort by 80% and improving accuracy by 45% across 100K+ monthly audits.* Constructed high-performance backend infrastructure using MySQL and microservices architecture, achieving a 75% reduction in query latency and processing 200K+ daily transactions with 99.99% uptime.* Integrated advanced caching solutions with Redis and DynamoDB, decreasing overall database strain by 70% and boosting application throughput metrics by up to 85%.* Designed scalable RESTful APIs and GraphQL endpoints handling 50K+ concurrent requests, reducing API response time by 65% through advanced caching strategies.Technical Competencies:* Full-Stack Development: JavaScript, Node.js, Express.js, MySQL, PostgreSQL, React.js, Redux, HTML5, CSS, jQuery.* Cloud Technologies: AWS (Lambda, EC2, S3, CloudFront, IAM, CloudFormation, CloudWatch).* DevOps and Tools: GitHub, CI/CD, RESTful APIs.* Software Architecture & Design: MVC Architecture, Microservice Architecture.

Architected a mission-critical data integration platform handling 500K+ daily records from law enforcement agencies (ODRC/CPD), achieving 99.99% data accuracy through robust ETL pipelines. Key Achievements:* Crafted a fault-tolerant data streaming architecture using Apache Kafka and custom ETL workflows, reducing data processing latency by 75% and enabling real-time analytics. * Created an intelligent document processing system using RAG-based LLMs, automating extraction from unstructured data with 95% accuracy and reducing manual processing time by 85%. * Enhanced database performance through advanced indexing and partitioning strategies, reducing query response time by 70% while maintaining high availability across MySQL clusters. * Built a comprehensive monitoring and alerting system using Prometheus and Grafana, achieving 99.9% system uptime and reducing incident response time from hours to minutes.* Streamlined infrastructure deployment using Ansible and Terraform, creating reproducible environments with zero-drift and reducing system provisioning time by 80%.Technical Competencies:* DevOps and Tools: Apache Kafka, ETL workflows, Prometheus, Grafana, Ansible, Terraform, Docker, Kubernetes.* Data Science: RAG-based LLMs, NLP, OCR. * Databases: MySQL, PostgreSQL, Cassandra, Redis, DynamoDB, Elasticsearch.* Programming Languages & Frameworks: Python, Java, .NET.* Cloud Technologies: AWS (Lambda, EC2, S3, CloudWatch).

Architected an enterprise-scale automation framework using Python and .NET, reducing operational costs by 60% and automating 1000+ daily workflows across client environments. Key Achievements:* Developed high-performance payment gateway microservices using .NET Core and Docker, processing $10M+ monthly transactions with 99.999% reliability and PCI DSS compliance. * Reduced deployment time by 70% and achieved zero-downtime deployments across 200+ production servers by building resilient CI/CD pipelines using Jenkins and GitFlow. * Improved resource utilization by 65% and enabled auto-scaling capabilities by orchestrating containerized applications using Docker and Kubernetes. * Maintained 99.99% uptime across diverse server infrastructure and reduced MTTR by 75% by orchestrating a comprehensive monitoring solution using Prometheus and Grafana.

Engineered a high-performance chatbot platform using React and Redux, optimizing user interactions for 10K+ monthly active users and achieving 99.9% system reliability.Key Achievements:* Developed an end-to-end testing framework using Jest and React Testing Library, achieving 95% test coverage and reducing bug reports by 60%. * Created advanced NLP models using TensorFlow and custom ML pipelines, improving chatbot response accuracy by 40% and reducing user escalations by 65%.* Optimized frontend performance through advanced caching and lazy loading, decreasing frontend latency by 50% and improving user retention by 35%.