I started in QA — which means I've always thought about what breaks. I moved through automation, full-stack dev, SRE, and security because each layer exposed a new class of problems. Now as a DevSecOps practitioner and Cloud Architect, I work at the intersection of all of them — and I use AI to accelerate every step.

• 🧪 QA & Testing: automation, shift-left testing, quality gates
• 🏗️ IaC: Terraform, OpenTofu
• ☁️ AWS: multi-account architectures, FinOps
• ⚙️ Platform Engineering: Kubernetes, GitOps, internal developer platforms
• 🔭 SRE: observability, reliability engineering, incident management
• 🔐 Security: policy enforcement, compliance automation, zero-trust
• 🤖 AI-Native Engineering: autonomous AI agents, Claude Code, internal prompt libraries, LLM-powered workflows
• 🧑‍💼 Leadership: project management, team building, client advisory

"If you touched it twice, it should be code."

• Define the problem first — most failures come from solving the wrong thing
• LLM as collaborator, not autocomplete — I steer it, validate the output, then make the minimal right change
• Don't reinvent the wheel — find the right tool, library, or pattern and make it fit
• Security at every layer — design, code, infra, ops; not bolted on at the end
• Ship the minimal right change — less surface area, less risk, easier to reason about

I've gone deep on making AI a core part of how engineering teams operate — not just as a productivity hack, but as a fundamental shift in how work gets done.

What that looks like in practice:

• Claude Code & agentic workflows — using Claude Code for autonomous coding tasks, code review, and infrastructure generation end-to-end
• Claude Skills — building custom Claude Skills that plug domain-specific knowledge and tooling directly into developer workflows
• Internal prompt libraries — building versioned, reusable prompt libraries that encode your team's standards and patterns so every engineer benefits from collective knowledge
• Autonomous AI agents — designing multi-agent pipelines that handle repetitive ops tasks, security audits, and compliance checks without human-in-the-loop for every step
• AI-native team transformation — helping engineering orgs move from "AI as a tool" to "AI as a team member": workflows, guardrails, evaluation loops, and culture
• LLM-powered platform tooling — embedding LLMs into internal developer platforms to reduce toil and accelerate onboarding

If you're thinking about how to actually operationalize AI in your engineering org — not just add a chatbot — let's talk.

Dealing with messy infrastructure, security gaps, AI transformation, or just need to go faster? Reach out.