Projects
I help talent teams improve process, optimize their TA stack, and build infrastructure that makes hiring swift and efficient. The goal is always the same: eliminate the rote work so recruiters can spend more time with candidates, where it actually matters.
Every service I offer is grounded in having built the same systems myself. The projects below are how I trial modern technologies before bringing them to clients at scale.
From Lever to Greenhouse, Greenhouse to Ashby, or building from scratch. Scoping, configuration, data migration, and integrations with HRIS, scheduling, and interview intelligence tools.
Finding where hiring accumulates drag and eliminating it. Workflow redesign, structured interview frameworks, SLA accountability, and the reporting that keeps it honest.
The right tools, configured correctly: AI scheduling, interview intelligence, and custom agentic workflows that remove manual work from your team's plate permanently.
An experienced head of talent for teams in transition. Full ownership of hiring strategy, team direction, stakeholder management, and executive reporting.
A structured evaluation of your current tooling: what's working, what's not, where the gaps are, and a concrete plan for what to change and in what order.
Dashboards that turn recruiting activity into business intelligence. Hiring velocity, stage conversion, DEI metrics, and the data structures that make future analysis possible.
Built with Astro, deployed on Netlify. Minimal by design.
The same approach applies to every client engagement: integrate the right tools, automate what's repetitive, and ship something that actually works.
ActiveRunning local LLMs on personal hardware with a fully self-hosted services stack. The goal: privacy-first AI tooling that works offline, responds instantly, and doesn't phone home. A playground for agentic experiments and automation workflows.
Running AI at the infrastructure level means I can evaluate and recommend tools from the inside, not just the vendor's sales deck.
ActiveBuilding AI agents that take action across tools, not just answer questions. Current examples include an email triage agent that classifies and surfaces high-signal messages without opening a tab, and a calendar agent that handles scheduling coordination, surfaces conflicts, and keeps meetings from becoming a second job. I use Obsidian as a thinking environment for all of these projects, capturing reasoning and decisions in a way that feeds directly back into the agents as context.
A direct proof of concept for the AI and automation work I do with clients: real agents, eliminating real manual work, running in production.
ActiveAn AI notepad that captures meeting context through local audio and screen recording rather than joining calls as a bot. Using it to surface actionable items from client conversations and hiring manager syncs without interrupting the flow of the meeting.
The kind of tooling that could change how recruiting teams capture and act on hiring manager conversations: no bots, no awkward introductions, just clean notes and next steps.
EvaluatingA multi-stage ETL pipeline built on a personal music library of 50,000 tracks. The extract stage ingests metadata from audio file tags and the Plex API. The transform stage normalizes genre taxonomies across 150+ canonical categories and extracts acoustic features (BPM, key, energy, danceability, timbral fingerprints) using Essentia, with a separate ML inference pass using a Discogs EffNet model for genre classification across 400 styles. The load stage writes to SQLite, powering a self-hosted playlist engine that queries the processed data by acoustic parameters and genre filters and pushes results directly to Plex.
The pipeline runs nightly via cron, is resume-safe across interruptions, and handles subprocess isolation for files that cause library crashes. The same dataset that started as a music project is now a foundation for broader ML experimentation.
Tools: Python, Essentia, TensorFlow, SQLite, Flask, Docker, Plex API, mutagen
The ETL architecture, ML pipeline, and data modeling here translate directly to recruiting analytics: stage conversion, time-in-stage anomalies, and the signals that predict outcomes.