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Senior DevOps/Infrastructure Engineer - 25+ years in tech, AWS, Kubernetes, CI/CD, Datadog. Currently seeking remote or Tampa Bay hybrid roles.

Automation
DevOps
GitHub Actions
Performance Monitoring
Terraform

I've been in technology since 1996, starting with building and selling PCs and working my way through every layer of the stack. For the last 7+ years I've focused on DevOps, platform engineering, and infrastructure automation. Most recently I spent 4.5 years as the sole infrastructure engineer at a banking SaaS company, where I owned the full stack for 87 customers and $5M in annual revenue. The work I'm most proud of from that role: I built a Datadog observability stack from scratch where no application visibility existed before, automated customer onboarding from an 8-hour manual process down to under an hour, and led the migration from on-premises hosting to AWS that supported 117% customer growth without adding infrastructure headcount. I also held a VP of Engineering title at the prior company, so I'm comfortable leading teams as well as building systems. My core stack is AWS, Kubernetes, CloudFormation, Docker, GitHub Actions, and Datadog. I've done cloud migrations, cost optimization (50% infrastructure reduction), CI/CD pipeline design, database administration, security compliance (SOC 2, pen test remediation), and operational documentation at scale. I'm looking for senior DevOps, SRE, platform engineering, or infrastructure roles - remote or Tampa Bay hybrid. I'm drawn to teams where infrastructure ownership is real, not bureaucratic, and where the engineer closest to the problem has the authority to fix it. I bring 25+ years of doing exactly that.

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Technical leader and software engineer driven to deliver innovative technical solutions to real-world problems

AI
AWS
Laravel
.NET Core
Node.js

My experience across the full software lifecycle enables me to deliver products with strategic value. With over 15 years of architecting and developing scalable solutions across enterprise and startup environments, my experience is diverse, yet deep in a few stacks. I've immersed and delivered results in varying environment from building serverless AWS architectures at Prologis and Ally Financial, to microservice migrations at Accenture, to ground-up SaaS platforms. My current work at NextKourt (AI-native platform enabling Parks & Recreation departments to run unstaffed venues autonomously) involves leading product strategy, design, and engineering. Building a platform from scratch has sharpened my ability across the stack: product strategy to architecture to DevOps. My last corporate role was a Technical Lead at Prologis, leading a scrum team, and delivering a centralized documentation repository within the Prologis Customer Portal, driving 200 new user registrations. A recent passion of mine is AI development workflows, particularly in discovering areas where AI excels at augmenting developers, and areas devs need to proceed with caution. Recently built a complete Claude Code automation workflow that orchestrates the full feature development lifecycle: Jira integrated spec planning, implementation, Playwright driven UAT, PR generation, code review, and issue transitions. It's cut my development cycles by roughly 40%, and changed the way I ship software. Where I'm most at home is with taking complex business problems and turning them into clean, scalable, and value-driven systems. My sweet spot is in architecting distributed systems, and robust APIs and services. Technology stacks: NodeJS/TS, .NET/C#, Laravel/PHP. I'm looking for part-time or fractional opportunities (10 - 20 hours per week) to apply my leadership and engineering. If you need someone who can own tracks end-to-end, from stakeholder alignment to engineering to delivery, let's talk.

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AI/ML Engineer

CodeBoxx
AI
C#
DevOps
Python
React

I’m an AI and full-stack engineer with over a decade of experience building production-grade systems that operate at scale. My path into software started with distributed systems and backend engineering, and over time evolved into designing and deploying machine learning and LLM-powered products that move from research to real-world impact. I’m most energized when bridging the gap between experimentation and reliable production systems. Recently, I’ve focused on agentic AI systems—designing workflows that combine LLMs, retrieval, evaluation, and human-in-the-loop controls to solve real product problems. I’ve led the development of scalable inference services, optimized data pipelines for unstructured data, and improved model performance through rigorous experimentation and observability. Across roles, I’ve consistently worked in fast-moving environments where clarity, ownership, and engineering judgment matter more than shiny tools . What sets me apart is my ability to operate across the entire stack: from model selection and prompt design to backend services, cloud infrastructure, and user-facing integrations. I care deeply about building systems that are not only intelligent, but also reliable, maintainable, and aligned with business outcomes. I’m passionate about mentorship, thoughtful system design, and avoiding unnecessary complexity—choosing the simplest solution that actually works at scale. If you’re looking for someone who can turn ambiguous AI ideas into durable, production-ready systems, I thrive in that space.

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