AI Platform / ML Observability

• Full-stack implementation of a secure GenAI observability system, including a high-throughput backend store built on Golang, Cassandra, and Kafka/Flink; as well as integral OpenTelemetry collection in internal Python MCP libraries

• Development of a Kubernetes controller reconciling cluster-external resources as an initial component of an ML Platform Control Plane

• Full-stack support and extension of experiment-tracking and hyperparameter-tuning services as components of an ML platform

Attended a self-guided educational retreat for programmers, with focus areas including:

• deep learning and basic fine-tuning of AI models via the Fast.AI course, supported by web scraping via Jupyter to assemble a custom dataset and Python package management to execute models locally on M1 Macs via the Metal Performance Shaders framework

• the Rust programming language

• Reading Designing Data Intensive Applications with a discussion group

• Refactored core AI-driven metrics computation functionality from Pandas PoC to complex Postgres query-generation to ”bring computation to the data”

• Refactored complex interlocked NLP-powered enrichment service chain to single ”enrichment service” to precache metrics for 1000x speedup for users

• Built extensible downstream score sync service to support mulitple API destinations

• Updated in-house Python ML dependency management to run locally on ARM64 M1 Macs

• Simple full-stack React work to support full-stack feature implementation and Typescript migration

• Migrating and building functionality from a Django monolith hosted on Rancher to scalable Scala & Kotlin microservices hosted on Kubernetes

• Piloting implementation of Scala and Kotlin Kafka event-driven microservices

• Serving in developer on-call rotation to resolve production incidents

• Picking up React/Redux and extending Django to deliver high-priority projects

• Built and extended features and ticket-provider API integrations for a leading theater ticket app's backend RESTful API and internal line-of-business applications

• Architected and began extraction of monolithic Groovy/Grails services to Go/ Kubernetes microservices

• Rapidly architected, implemented, loadtested, and launched new features enabling TodayTix to land lottery deal for 2018's biggest show, Harry Potter

• Systematically investigated usage of and deprecated V1 API components in order to eliminate vast quantities of legacy code and enable faster bugfixes and implementation of new features

• Maintained and modernized Ansible deployment tooling