Technical Vision + Practical Delivery

I’m the rare technical leader who architects infrastructure, writes code and connects it to business value.With 25 years in tech spanning enterprise solutions, sales, DevOps, and security, I’ve learned that great systems mean nothing if they don't solve real problems. And by drawing on all of that, I bridge the gap between complex technical innovation and measurable business outcomes. No hand-offs, no silos—just straightforward expertise built on actually understanding both sides of the equation.Curious about the impact?Check out the Work section below to see how I've transformed technical challenges into business wins.Let’s build something together that just plain works!

Client Testimonials

Case Study: SCWRL (Streamlined Clinical Writing & Rapid Lexicon)

Overview:
SCWRL is an AI-powered application designed to streamline the documentation process for clinical therapists by automatically redacting Personally Identifiable Information, extracting key session information, and formatting compliant clinical notes that meet insurance standards.

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Project: CI/CD Pipeline with Terraform, AWS, Docker/Kubernetes, Helm, and Slack Integration

Description:
Designed and deployed a fully automated CI/CD pipeline for containerized applications using modern DevOps practices. The system provisions infrastructure with Terraform and leverages GitHub Actions to deploy applications to AWS EKS with zero-downtime updates.Key components include:✔ Infrastructure as Code (IaC): Terraform provisions a production-ready AWS EKS cluster with secure networking, IAM roles, and OIDC authentication
✔ CI/CD Automation: GitHub Actions automates Docker image builds, pushes to Amazon ECR, and deploys to EKS using Helm charts
✔ Security & Authentication: Implements OIDC-based authentication, least-privilege IAM roles, and Kubernetes RBAC for secure access
✔ Monitoring & Notifications: Automated health checks and Slack alerts for build/deployment status and system health
✔ Enterprise-Grade Deployment: Ensures scalable, secure, and observable cloud infrastructure suitable for real-world production environments

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GitHub Repo | Architectural Diagram | Health Check | Slack Notifications

Project: Brewery Sales & Production Data Pipeline

Description:
Built a fully automated PoC ETL pipeline using Python to process brewery sales and production data, generating real-time insights on sales performance, production efficiency, and profitability.Key components include:✔ Automated Data Ingestion & Transformation using Python & scheduled cron jobs
✔ PostgreSQL Database storing raw and processed data
✔ Streamlit Dashboard for interactive data visualization
✔ Scalable Architecture designed for real-world production environments

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Live Demo | GitHub Repo | Architectural Diagram

Project: Checkout Redesign With E-commerce Payment Processing System

Description:
Designed and implemented a robust, full-stack e-commerce payment processing system with a modular architecture that delivers a seamless checkout experience. This project separates frontend and backend concerns for independent scaling and deployment, resulting in a highly maintainable system with enhanced reliability and security.Key components include:✔ Modern Frontend: React with TypeScript provides a type-safe, component-based architecture with responsive design using Tailwind CSS for all device sizes
✔ Scalable Backend: Express.js server implementing RESTful API patterns with comprehensive error handling and asynchronous payment processing via webhooks
✔ Secure Payment Processing: Stripe API integration with client-side tokenization, server-side verification, and proper error handling for failed transactions
✔ Data Persistence: MongoDB with Mongoose ORM for efficient data models storing products, customer information, and order history with proper security practices
✔ Transactional Communications: Robust email notification system sending order confirmations and transaction receipts using HTML templates and proper error fallbacks
✔ Deployment Strategy: Multi-platform approach with Vercel for frontend and Railway for backend, enabling independent scaling and specialized CI/CD workflows
✔ User Experience Focus: Intuitive cart management, streamlined checkout process, and transparent order confirmation with immediate email notifications

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Live Demo | Walk Through Video | Frontend Repo | Backend Repo | Architectural Diagram

Project: MisterLooperz - Custom YouTube Video Looping Platform

Description:
Whenever I'm building things, I get into a flow state best with music playing on repeat. YouTube has been my go-to source for tracks, but I've always found existing player solutions lacking in key features. So, after years of making do with imperfect options, I finally decided to build my own custom YouTube player with exactly what I wanted.MisterLooperz is an interactive web application that allows users to create custom loops from YouTube videos with precise time control. The application enables seamless video playback with user-defined start and end times, playlist management, and an innovative auto-play feature. Built with modern web technologies and responsive design principles, it delivers an intuitive user experience across desktop and mobile devices.Key components include:✔ Modern Frontend Architecture: React with TypeScript implementation providing type-safe code and component-based architecture with responsive design for desktop and mobile experiences
✔ YouTube API Integration: Secure implementation of the YouTube Player API with custom controls for precise video playback, looping, and auto-advancing functionality
✔ State Management: Efficient local storage implementation for persistent user playlists and preferences without requiring user accounts or backend components
✔ Custom UI Components: Purpose-built input components with mobile-optimized time entry that simplifies entering hours, minutes, and seconds on touch devices
✔ Performance Optimization: Carefully optimized render cycles with React memo and useCallback to ensure smooth playback across devices, even during orientation changes
✔ Responsive Design: Adaptive interface that reconfigures dynamically based on viewport size, ensuring optimal usability on both desktop and mobile devices
✔ Error Handling: Robust error recovery mechanisms for handling network issues and YouTube API limitations with graceful fallbacksThe application demonstrates advanced frontend development skills including API integration, custom component design, state management, and responsive layout techniques while delivering a polished user experience.

Live Demo | GitHub Repo

CI/CD Pipeline with Terraform, AWS, Docker/Kubernetes, and Slack Integration

Project Overview
This project implements a comprehensive DevOps solution that integrates Infrastructure as Code (IaC) with Terraform and a CI/CD pipeline using GitHub Actions. It demonstrates provisioning, deploying, and managing containerized applications on Amazon EKS with enterprise-grade DevOps practices.How It Works1. Infrastructure Provisioning with Terraform
• EKS Cluster – Terraform provisions a production-ready EKS cluster on AWS.
• Networking – VPC, subnets, security groups, and routing tables ensure network isolation.
• Node Groups – Auto-scaling worker nodes with proper IAM permissions.
• IAM Roles – Least-privilege roles for EKS, nodes, and GitHub Actions.
• OIDC Provider – Secure token-based authentication setup.
• ECR Repository – Private container registry for storing Docker images.
• State Management – Remote state storage for team collaboration.2. CI/CD Pipeline with GitHub ActionsContinuous Integration
• Trigger: Code push to main branch automatically starts the CI pipeline.
• Build: Code is containerized into a Docker image with version tracking (Git SHA tag).
• Store: The image is pushed to Amazon ECR (Elastic Container Registry).
• Notify: Slack updates the team on build success/failure.Continuous Deployment
• Trigger: Successful image build triggers deployment workflow.
• Auth: GitHub Actions authenticates with AWS using OIDC (secure token-based authentication).
• Deploy: Kubernetes manifests (Helm charts) define and deploy resources to the EKS cluster.
• Zero-Downtime Updates: Rolling deployments prevent downtime.
• Notify: Slack updates include deployment status and application URL.Monitoring & Health Checks
• Automated Checks: Runs every 30 minutes to verify pod health and app response.
• Slack Reports: Real-time alerts for system failures.
• Manual Triggers: Health checks can be manually run via GitHub Actions.Technology StackInfrastructure as Code (IaC)
• Terraform – Declarative cloud provisioning
• AWS Provider – Integration with AWS services
• Terraform Modules – Reusable components for EKS, VPC, IAM
• Remote State – Team collaborationCloud Infrastructure
• Amazon EKS – Managed Kubernetes
• Amazon ECR – Private Docker image registry
• AWS IAM – Secure authentication with OIDC
• AWS VPC – Secure networking (public & private subnets)
• AWS Load Balancers – Kubernetes-native load balancingCI/CD Tools
• GitHub Actions – Workflow automation
• Helm – Kubernetes package management
• Docker – Containerization
• kubectl – Kubernetes CLISecurity
• OIDC Authentication – Secure, short-lived tokens
• IAM Roles – Least-privilege access
• GitHub Secrets – Secure key management
• RBAC with aws-auth ConfigMap – Kubernetes IAM integration
• Security Groups – Network-level access controlsMonitoring & Notifications
• Health Checks – Automated monitoring
• Slack Integration – Deployment alerts
• Kubernetes Pod Monitoring – Real-time observabilityTechnical Challenges Addressed1. Infrastructure as Code (IaC)
• Fully automated EKS cluster provisioning with Terraform.
• Version-controlled Kubernetes resources using Helm.2. Secure Cloud Authentication
• Uses OIDC for GitHub Actions → AWS authentication (no static credentials).3. Kubernetes Authentication
• Maps AWS IAM roles to Kubernetes RBAC via aws-auth ConfigMap.4. Zero-Downtime Deployments
• Helm-based deployments ensure rollback capabilities.5. Observability
• Real-time Slack notifications for build & deployment status.
• Automated health monitoring of Kubernetes pods.Skills DemonstratedInfrastructure as Code
• Terraform for cloud provisioning
• Modular infrastructure design
• Remote state managementDevOps & Cloud Engineering
• CI/CD pipeline automation
• AWS service integration (EKS, ECR, IAM, OIDC, VPC)Security Engineering
• IAM Role-based access control
• Secure authentication workflows
• Secrets managementKubernetes Expertise
• EKS cluster management
• Helm chart development
• Kubernetes RBAC & IAM integrationMonitoring & Observability
• Application health monitoring
• Slack notifications for deployments
• Diagnostic toolingTerraform Infrastructure DetailsVPC & Networking
• Custom VPC with multi-AZ private/public subnets.
• NAT Gateways for private subnet connectivity.
• Security groups with least-privilege rules.EKS Cluster
• Managed EKS control plane with secure endpoint access.
• Auto-scaling worker nodes with IAM permissions.
• Kubernetes version management with automated add-ons.IAM Configuration
• OIDC authentication for GitHub Actions.
• IAM role assumption policies for secure AWS-Kubernetes integration.ECR Repository
• Private Docker registry for image storage.
• Lifecycle policies for image cleanup.Security Controls
• Security groups with granular access policies.
• Private networking for worker nodes.Future Enhancements
• Additional Terraform modules for AWS services (RDS, ElastiCache).
• Canary deployments for safer production releases.
• Automated testing (unit, integration, security).
• Monitoring integration (Prometheus, Grafana).
• GitOps implementation (ArgoCD, Flux).
• Disaster recovery with cross-region failover.This project represents a sophisticated enterprise-grade DevOps architecture, demonstrating advanced Terraform, Kubernetes, AWS, and CI/CD skills. It provides a scalable, secure, and automated infrastructure for modern cloud applications.

Project Overview
The Checkout Redesign is a robust, full-stack e-commerce payment processing system that I built to demonstrate modern web development practices and industry-standard payment integration. This project reimagines the online shopping checkout experience with a focus on user experience, security, and reliability, using a modular architecture designed for deployment flexibility.Technologies UsedFrontend
• Framework: React with TypeScript
• Routing: React Router for navigation
• API Communication: Axios
• Styling: Tailwind CSS for utility-first styling
• Deployment: VercelBackend
• Runtime: Node.js
• Framework: Express.js
• Database: MongoDB with Mongoose ODM
• Payment Processing: Stripe API integration
• Email Notifications: Nodemailer
• Deployment: RailwayArchitecture & Deployment Strategy
The application follows a decoupled architecture with separate frontend and backend codebases, allowing for:
• Independent scaling of services
• Deployment to specialized platforms (Vercel for the frontend, Railway for the backend)
• Easier maintenance and development workflow
• Flexible development environmentsThis modular approach enables continuous deployment of the frontend without affecting the backend services, and vice versa.Key Components1. React Frontend
The frontend is built with React and TypeScript, providing a type-safe and maintainable codebase with:
• Component-based architecture for reusability
• Responsive design for all device sizes
• State management for cart and checkout flow
• Form validation for shipping and payment information
• Order summary and confirmation views2. RESTful API Backend
The core of this project is a scalable Express.js server implementing RESTful API patterns. It handles:
• Product management
• Order processing
• User authentication
• Integration with third-party services for payments and notifications
• Error handling and logging3. Payment Processing System
I implemented a comprehensive payment flow using Stripe's API, handling:
• Secure payment intent creation
• Client-side tokenization
• Server-side verification
• Webhook processing for asynchronous events
• Error handling for failed transactions4. Data Management
The application uses MongoDB for data persistence with carefully designed schemas for:
• Products with detailed metadata and categorization
• Customer information with proper security practices
• Order history with transaction details
• Sale and promotion management5. Transactional Email System
The system includes a robust email notification service that:
• Sends order confirmations
• Handles transaction receipts
• Manages error fallbacks using Ethereal for development
• Implements HTML email templatesUser Flow
The application guides users through a seamless shopping experience:
1. Cart Management: View, update quantities, and remove items
2. Checkout Process: Enter shipping and payment information
3. Order Confirmation: Review order details with confirmation
4. Email Notification: Receive transactional emails with order statusTechnical Challenges & SolutionsModular Deployment Architecture
I designed the system with separate frontend and backend codebases to allow for independent deployment and scaling. This architecture:
• Enables use of specialized hosting platforms for each component
• Facilitates CI/CD workflows for each codebase
• Improves fault isolation between system componentsRobust Error Handling
I implemented a multi-layered error handling approach to gracefully manage payment failures, network issues, and data validation problems. This includes:
• Detailed logging
• Appropriate HTTP status codes
• User-friendly error messagesAsynchronous Processing
The system handles asynchronous payment events through webhooks, ensuring that order status remains accurate even when payment processing happens outside the main request-response cycle.Environment Configuration
I built the application with environment-aware configuration, allowing it to adapt to development, testing, and production environments with appropriate settings for each context.Skills Demonstrated
✔ Full-stack Development: End-to-end implementation of frontend and backend components
✔ Modern Frontend Development: React with TypeScript, component architecture
✔ Backend Development: Building scalable server architecture with Node.js and Express
✔ Database Design: Creating efficient data models and query patterns
✔ API Integration: Working with third-party services (Stripe, email)
✔ Security Implementation: Handling sensitive payment and user data securely
✔ DevOps & Deployment: Multi-platform deployment strategy (Vercel for frontend, Railway for backend)
✔ Error Handling: Creating resilient systems that gracefully handle failures
✔ Technical Documentation: Thorough code commenting and system documentation
✔ System Architecture: Designing a modular, maintainable application structureThis project showcases my ability to build complex, production-ready web applications with a focus on modern architecture, reliability, security, and user experience.• Note: If you'd like to try out the order and payment flow on the demo site use the following details:
Card #: 4242424242424242
Name On Card: <Your Name>
Card Expiration: 12/34
Card CVV: 345

Project OverviewSCWRL is an AI-powered application designed to streamline the documentation process for clinical therapists by automatically redacting Personally Identifiable Information (PII), extracting key session information, and formatting compliant clinical notes that meet insurance standards.Challenge
Therapists face significant administrative burden with clinical documentation, and this Client was experiencing exactly that. Before we started working together, this Client was spending an average of 40 minutes per therapy progress note due to insurance requirements and specifics of the different platforms they use. With a caseload of 30 patients, that meant documentation was taking almost 20 hours of UNPAID work per week. That time was also not spent on helping more patients, or working on their business. All of this was adding to burnout and stress which leads to:
• Reduced patient care time
• Professional burnout
• Potential compliance issues with HIPAA/insurance requirements
• Increased fear of insurance clawbacksSolution
A comprehensive web application that:
• Automatically redacts PII from therapy session notes
• Extracts structured data from clinical text
• Analyzes content for insurance compliance
• Generates well-formatted, professional therapy notesTechnologies UsedCore Application
• Language: Python
• Frontend Framework: Streamlit
• Natural Language Processing (NLP) Toolkit: spaCy
• Prompt Orchestration: LangChain
• Version Control: Git/GitHub
• Deployment: Railway.ioAI Models
• OpenAI GPT-4o (for deep clinical reasoning)
• Anthropic Claude Sonnet 3.7 (for nuanced therapeutic context)
• Model Control: UI toggle and adaptive prompting based on selected model
• API Management: Independent API keys for OpenAI and AnthropicArchitecture & Design
The app uses a flexible multi-model NLP pipeline with distinct components:
• PII Redaction Engine:
- Redacts names, emails, addresses, and zip codes
- Uses spaCy Named Entity Recognition (NER) and custom regex
- Whitelists DSM codes and clinical terms to prevent over-redaction
• Structured Data Extraction:
- Identifies session elements using LangChain pipelines
• Compliance Analysis:
- Validates content against insurance documentation standards
• Note Generation:
- Formats professional notes optimized for audit-readiness
• Output Normalization:
- Ensures consistent output across AI modelsProject MethodologyDiscovery Phase
• Client Interview: Explored current workflows and documentation pain points
• Documentation Analysis: Reviewed past session notes to identify terminology and formatting patterns
• Workflow Mapping: Charted existing process to understand automation opportunities
• Requirements Prioritization: Defined MVP through collaborative rankingIterative Development
• Sprint Cycles: Feature delivery in agile 1-week increments
• PII Redaction First: Built core safety feature before expanding to AI integration
• Weekly Demos: Regular client check-ins for feedback and direction
• Prototype-Refine Loop: Early testing of major components before full build-outTesting & ValidationPII Detection
• 100+ synthetic notes tested
• Benchmarked vs. manual redaction
• False positives reduced by ~70%
• Regex and NER patterns refined through error analysisModel Performance
• Clinical note test cases evaluated for:
- Accuracy
- Terminology usage
- Insurance format compliance
• Blind client scoring: Rated 1–5 across key quality metrics
• Model profiles: Documented strengths of OpenAI vs. Anthropic per taskUser Experience Testing
• Guided sessions with client
• Pre/post implementation task completion time measured
• UI simplified based on user feedback
• Added tooltips, compliance meters, and model descriptionsTechnical Challenges & SolutionsChallenge 1: Robust PII Redaction
• Problem: Over-redaction of clinical terms and missed edge cases (e.g., "user@gmail")
• Solution:
- Whitelist for DSM terms and clinical codes
- Regex patterns for irregular address formats
- Zip code isolation for improved accuracy
- Partial email detection patterns
- Fine-tuned spaCy NER modelChallenge 2: Seamless Multi-Model Integration
• Problem: Differences in output formatting and clinical understanding across LLMs
• Solution:
- Unified abstraction layer for input/output normalization
- Model-specific prompts for task optimization
- Fallback system for API errors
- Adaptive temperature/top-p based on model/task pairing
- Caching to reduce latency and costResults
• 57% reduction in documentation time — client gained back over 11 hours per week previously spent on note writing
• Ensures HIPAA compliance through secure handling of PII
• Reduces fear of insurance clawbacks by standardizing notes to meet medical necessity and payer documentation standards
• Positive client feedback on usability, flexibility, and impact on workflow
• Improved in-session focus due to reduced documentation-related stress
• Enhanced clinical flow with a streamlined, consistent documentation processClient Testimonial
“SCWRL has completely transformed my documentation process. What used to take nearly 40 minutes per session note—and came with a lot of stress—now takes an average of just 17 minutes. I’ve saved a HUGE amount of time and can now focus more on seeing patients instead of spending hours on unpaid admin work. As a therapist in private practice, that’s a game changer. On top of that, the quality of my notes has actually improved, and the process feels much more streamlined. I also appreciate the flexibility to choose between different AI models based on the complexity of each case.”
— AK, LCSW - Clinical DirectorCost-Benefit Analysis
• At 30 patient notes per week, SCWRL saved the Client around 46 hours per month compared to their previous method of note writing
• This time savings equates to over $5,000 in billable time that can be reclaimed
• LLM usage cost for this volume is less than $8 per month
• This means for every $1 the Client spends running SCWRL they’re getting back about $631 in net returns. Not a bad ROI.

SCWRL UI | Architectural Diagram

Got a technical challenge that needs solving?A project that's stuck in the planning phase? Or maybe you're looking for someone who can translate complex requirements into working systems?I bring 25 years of hands-on experience across the technical spectrum—from architecture design to implementation and beyond. No buzzwords, no overcomplication. Just practical expertise that delivers measurable results.Whether you need a consultation, a technical assessment, or a complete solution, the first conversation is the hardest part.The rest? That's where I come in.Reach out today!