This is the implementation record: independent code and test fixtures, production engineering case studies, and published security evidence. I keep those categories separate so it is clear what I built, what I verified locally and what remains private. There are also mobile apps, audio projects and small tools I made because they were interesting.
Professional systems
Mobile, backend and security work from enterprise environments. Architectural decisions and failure paths, not private customer artifacts.
I trained a small audio-activity classifier with NumPy, built clean and noisy held-out sets, and ran controlled feature and augmentation ablations using repeatable seeds and explicit precision/recall/F1 results.
I implemented a voice-to-CRM event service with HMAC-verified webhooks, replay-window checks, tenant-scoped idempotency, duplicate detection and recovery after simulated downstream outages.
I implemented and trained a small nonlinear vector-quantized audio autoencoder with a learned encoder, decoder and discrete codebook, then compared measured reconstruction error to an 8-bit companding baseline.
I worked on session-recovery controls that made identity checks, step-up decisions and failure states explicit instead of weakening authentication during recovery.
I worked on session-recovery controls that made identity checks, step-up decisions and failure states explicit instead of weakening authentication during recovery.
Backend auth services, state machine modeling, observability instrumentation