Projects
Selected project and case-study style work, written at a level that keeps implementation details confidential while showing how I think.
These are representative case studies from my work across backend systems, product delivery, reliability, and AI-assisted engineering. They intentionally avoid confidential implementation details, but they show the kinds of problems I like owning.
End-to-end Flexben feature delivery
Problem: A major Welfare/Flexben feature needed to move from product discovery to launch while keeping Product, frontend, mobile, and backend teams aligned.
What I owned: Backend technical analysis, delivery planning, API and integration decisions, implementation, review, and production readiness.
How I approached it: I translated business requirements into concrete technical specifications, surfaced tradeoffs early, coordinated integration points across teams, and stayed close enough to the code to keep decisions grounded.
Market signal: This is the kind of work I want to be known for: not only writing backend code, but leading a technically complex feature from ambiguity to production.
AI agents for engineering throughput
Problem: Repetitive engineering tasks, low-level code generation, migration work, and boilerplate can consume too much attention from engineers.
What I built: Claude subagents and agent-style workflows that automate recurring code tasks while keeping human review and design judgement in the loop.
How I approached it: I focused on workflows where AI has concrete leverage: code scaffolding, test skeletons, repetitive transformations, technical analysis, and local experimentation.
Market signal: I use AI as an engineering multiplier, not as a buzzword. The goal is faster delivery with the same or higher quality bar.
Event-driven year-end order processing
Problem: Flexben year-end operations required many order-closing workflows to run simultaneously, creating bursty workload and reliability pressure.
What I designed: A high-performance event-driven fan-out system to distribute work and make the processing model easier to scale and operate.
How I approached it: I separated orchestration from execution, leaned on event-driven patterns, and considered failure isolation so operational pressure would not become product risk.
Market signal: I am comfortable with backend systems where correctness, scalability, and operational timing all matter at once.
Payment resilience and circuit breakers
Problem: Peak-load scenarios can turn isolated downstream instability into wider platform risk if failure is allowed to cascade.
What I built: Circuit breaker mechanisms and resilience-oriented backend changes for payment-adjacent services.
How I approached it: I treated resilience as part of user experience: contain failure, make behavior predictable, and give systems a controlled way to recover.
Market signal: I care about the production behavior of systems, not only whether a feature passes happy-path tests.