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Michael Hamilton

Core Competencies

Deep Technical Detail

Architecture & Platform Modernization
API-first, microservices-based architectures; domain boundaries and service ownership Platform migration/pivots under real constraints (time, reliability, teams, customers) CI/CD pipelines and release governance aligned to production operations
Release Engineering & Operability
OTA update strategies, rollout gates, rollback discipline, and change control Observability expectations: logs/metrics/traces, SLO-minded monitoring, incident hygiene Staged deployments (small batches → validation → broad rollout)
Security & Governance (Security-first by default)
AuthN/AuthZ patterns, least-privilege access, auditability, secure-by-design workflows Controls for sensitive data handling (especially document workflows) Evaluation/guardrails mindset for AI-enabled systems (quality gates, failure modes)
AI-Enabled Workflows (Practical, production-oriented)
LLM/RAG-ready workflow design: retrieval strategy, evaluation approach, guardrails Document intelligence and contradiction detection workflows integrated into user tools Focus on trustworthy automation over “demo magic”
Edge/Real-Time Systems (LiDAR + vehicle/field deployments)
Real-time sensing and detection/awareness workflows; edge analytics constraints Vehicle-based systems: integration, deployment discipline, operational continuity Reliability hardening in constrained environments
Cloud/DevOps Tooling
Containerized deployments (Docker), build/release pipelines, artifact management AWS experience (admin/operations, multi-account environments) where relevant Practical performance monitoring: CPU/memory/disk/temperature/GPU metrics
Desktop & Automation (when useful)
WinUI 3 desktop application patterns (MVVM), Python↔C# interop Video/camera workflows with OpenCV where applicable to tooling/testing
Selected “Built It” Examples
Implemented OTA update process + rigorous release governance for high-quality deployments. Architected a microservices-based system designed for scalability and rapid feature integration. Led security-first AI document workflows with evaluation/quality gates and guardrails. Built operational discipline around observability expectations and production-grade controls.