The five-person ELA development team was shipping .NET features at a traditional pace — handwritten CQRS handlers, manually scaffolded EF Core configurations, and inconsistent test coverage across modules. AI tools were available to the organization but used inconsistently: some developers leaned on Copilot for autocomplete, others ignored it entirely, and no one had a shared convention for prompts, context, or AI-assisted code review. The result was uneven velocity, duplicated effort across the team, and no defensible way to measure whether AI tooling was actually helping. On a federal contract with hard delivery milestones, the team needed a repeatable workflow rather than ad-hoc AI use.

Defined and rolled out team-wide AI-native engineering standards covering spec-first development, prompt and context conventions, and AI-augmented code review checkpoints. Integrated Claude Code, GitHub Copilot, and M365 Copilot into the daily workflow with clear guardrails on what each tool was best for. Established markdown-based Technical Design Documents as the single source of truth that AI agents would consume to scaffold new modules, then trained the team on the workflow through mob-coding sessions and code reviews.

Shifted the team from ad-hoc AI use to a defined, repeatable workflow with shared conventions and review gates. Time-to-first-working-build for new modules dropped meaningfully once TDD-driven scaffolding became the default — handlers, EF Core configs, and tests generated from spec instead of typed by hand. Code review quality improved because reviewers had a clear checklist for AI-generated code, and onboarding got faster because the standards doc and CLAUDE.md context files gave new developers a runway. The team built a defensible story for leadership that AI tooling wasn't just autocomplete — it was a productivity multiplier with measurable gates.

The FEMA ELA platform's primary data store ran on MySQL 5.7, which was approaching end-of-life and lacked the security, performance, and JSON/CTE features the .NET application increasingly relied on. On a federal platform supporting emergency lodging during disaster events, the database also had to demonstrate verified high-availability failover — a documented, repeatable test, not a hope. Upgrading in place risked breaking EF Core data access, stored procedures, and existing query plans, and a botched failover during an active disaster response would have been a serious incident.

Owned the MySQL 5.7 → 8.0 upgrade plan end-to-end and ran the HA failover validation exercise alongside it. Authored the TDD covering compatibility analysis, upgrade path, rollback strategy, and the failover test protocol. Coordinated with Corpay infrastructure, the FEMA stakeholders, and the dev team to schedule the cutover, then executed and signed off on the failover drill against the upgraded cluster.

Brought the ELA platform onto a supported MySQL version with stronger security defaults and modern SQL features, eliminating EOL risk on a federal contract. The HA failover drill produced a signed-off, repeatable runbook — meaning the on-call team had verified evidence of failover behavior rather than a theoretical design. EF Core data access continued working without regressions across the full integration test suite, and the Azure Blob backup pipeline gave the platform a clean DR posture going into disaster season.

The existing monolithic .NET Framework 4.8 application struggled to scale under growing user demand (10,000+ daily users), suffered from tightly coupled components that slowed feature delivery, and lacked proper API governance. Issue detection was slow and reactive, leading to extended downtime and poor user experience.

Redesigned the application architecture from monolithic to microservices using Docker and Azure Kubernetes Service (AKS). Migrated legacy .NET Framework 4.8 applications to .NET Core and integrated Java microservices. Implemented Azure API Management for centralized API governance and Azure Monitor with Application Insights for proactive performance monitoring. Built serverless event-driven applications using Azure Functions.

Improved system scalability by 40% and enabled independent service deployments. Boosted application performance by 30% through the .NET Core migration. Reduced issue detection time by 50% and maintained 99.9% application uptime. Reduced infrastructure overhead through serverless architecture. Improved API governance and security across the platform.