Mike Hall

Software Engineer and architecture lead who recovered lost knowledge and made it actionable, helping make regulated customer workflows easier to understand, safer to change, and more observable across organizational boundaries.

• Mapped the loan acquisition and origination pipeline end-to-end from visit to booked loan across browser, mainframe, and Affiliate API, identifying seven distinct lending funnels.
• Built data-risk remediation and architecture-discovery practices that made legacy dependencies visible and safer to change.
• Diagnosed a production state-management defect affecting application completion and moved the system to a more durable session design without interrupting service.
• Established distributed observability across application and integration boundaries, improving the evidence available during incident response.
• Built communities of practice, mentoring loops, and handoff mechanisms that distributed technical ownership beyond a single engineer.

Software Engineer focused on backend stability, performance, and safe modernization for high-traffic Rails products.

• Improved search and asynchronous delivery systems while production traffic continued to flow.
• Led framework and database upgrades with a staged approach designed to protect service continuity.

Software Engineer on a large CRM platform, focused on testability, performance, and safer change inside a legacy backend.

• Replaced fragile data-access patterns with structured, testable queries and improved performance.
• Strengthened automated verification around core customer workflows, enabling safer refactoring.

Built local-first tools that help engineers and AI systems work with clear context boundaries, observable behavior, and human-reviewed change.

• Built tools that make technical context reusable across sessions without treating generated output as authority.
• Added observable diagnostics and bounded review steps to keep automated engineering work understandable and reversible.

Building a real-time tactical game as a laboratory for deterministic systems, product architecture, and carefully bounded AI-assisted engineering.

• Designed replayable state transitions so complex outcomes remain reproducible and independently reviewable.
• Built verification gates that test system behavior across the full lifecycle.

Building a Rails platform for structured research, semantic search, and human-directed automation.

• Unified fragmented sources into a searchable data model for downstream analysis.
• Designed local-first AI workflows with explicit safety boundaries around untrusted input and sensitive context.