Project

Building my first ML Lifecycle — a three-tier architecture for an ad Fallback CTR prediction — as a backend engineer without an AI background. The technical decisions I made, and what I learned through running it.

A retrospective on column-level encryption of sensitive data in a running service. Envelope encryption, DEK granularity decisions, the WHERE clause constraint that led to HMAC, and the migration automation Skill that spread the work across the org.

Retrospective on building a system that automatically identifies low-performing SSP inventory and throttles traffic to improve contribution margin. Covers the evolution from Imp Cost Ratio to a predicted contribution margin rate approach.

Designing a Nest.js-based fallback server while removing a legacy ad server. Why a horizontal layered architecture fit better than Nest.js’s default vertical module slicing for a single API with complex business logic.

How I reduced network costs and enabled instance downscaling by switching from full cache refresh to incremental refresh for campaign configuration data.

A retrospective on moving ad budget pacing from a fixed-rule scheme to a two-layer control loop — per-campaign learning sets the baseline, real-time correction absorbs drift.