I initiated this work after exploring how AI-assisted workflows could make courier migrations faster and more repeatable. I engineered a reusable migration framework, moved high-volume integrations from the Python monolith to Kotlin predispatch, documented the approach, conducted knowledge-transfer sessions, and mentored interns and teammates on subsequent service migrations.

I architected a common cancellation framework with courier reallocation and Kotlin routing, replacing fragmented per-courier implementations while preserving the behavior required by production integrations.

I owned the design and execution end to end: migrating audit logs from PostgreSQL to ScyllaDB, backfilling 15 days of forward-order data and 90 days of reverse-order data, and building a cron that archives each 15-minute window as Parquet in S3. Archives transition to Glacier after 30 days and expire after 180 days. The architecture supports normal volumes of roughly 2 million orders per day and sale peaks approaching 10 million.

While independently exploring OpenTelemetry traces, I found more than 100 daily server errors that had no useful application logs. I added complete request-lifecycle logging with OpenTelemetry, HyperDX, Grafana, and ClickHouse, traced the failures to a Ktor authentication flow that continued through downstream plugins after returning Unauthorized, fixed the termination behavior, and eliminated those production failures.

Deployments previously depended on my manager or me having local Elastic Beanstalk access. I established a Jenkins-to-AWS deployment pipeline so the full team could deploy independently, upgraded Kotlin and adopted the K2 compiler, and tuned Maven until builds ran reliably on the existing Jenkins infrastructure instead of requiring a larger dedicated machine.

I delivered webhook and polling-based tracking integrations using Kafka and Nomad jobs, enabling near-real-time shipment status updates across enterprise logistics workflows.

I optimized SQL transformations and introduced multithreaded Python ingestion to process large datasets more efficiently while improving downstream data quality.

I automated large-scale AWS S3 downloads, ingestion, and processing with Python so processed datasets could be delivered within minutes instead of through repetitive manual work.

I managed periodic extraction and transformation workflows and kept daily production operations reliable throughout the internship.

I developed authenticated vote-submission APIs and smart-contract integrations that supported transparent result verification.

I separated authentication and vote-submission workflows into modular REST endpoints, enabling independent backend testing and development.