Career record · verified against claims ledger 6d3ce1d2 Austin, Texas · remote-first · open to relocating
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Twice-exceptional product engineer. Fifteen years leading product, three spent building AI systems solo.

I turn how minds actually work into software that fits them.

01 How to read this

This record was drafted by my own AI infrastructure against a verified-claims ledger I maintain for exactly this purpose. Every figure below traces to public data or my own first-person account. Two impressive numbers were deleted during drafting because I could no longer verify them. You would have liked them.

The system that enforced that discipline is live at jonmick.ai. You can browse it, or call it: 1-928-706-9386 connects you to my external mind, which will happily discuss my work in the third person. I would rather you know my real strengths and how I actually work than meet a masked version of me.

This is the general version: same ledger, same claims, ordered by recency. Tailored versions exist per conversation and reorder the same facts for the reader.

02 The throughline

Make a complex system legible, then build tools that fit it instead of fighting it.

I ran that move on enterprise platforms for fifteen years. For the last three I ran it on the most complex system I had access to: my own cognition. Whole-genome sequencing, structural and functional brain imaging, months of neurofeedback, and AI as the synthesis layer. That work produced a working theory of how my mind holds and loses context, and then I built the infrastructure it needed, solo.

03 How I work

Drawn to broken systems.Give me the paused project, the undocumented codebase, the platform nobody can explain. At Liquidity Services I took over a client implementation that had been paused after ten months of quality problems and returned it to client-approved specifications that survived a major architectural pivot.
Mechanism before metric.I audited a legacy platform's most complex subsystem feature by feature against actual code: 64% of documented features verified in code, 36% documented but absent. Two decades of drift, quantified before anyone built on top of it.
Reads people and teams, then fits the tooling to them.Coaching Air Force product teams meant translating product practice into their operational culture instead of imposing mine.
Fluent fast.E-commerce, automotive, real estate, retail loyalty, auctions, defense-adjacent coaching. The domains keep changing; the embed pattern holds.
Pattern recognition across unfamiliar domains is the superpower.Twice-exceptional, and open about it. It is why a 20-year-old undocumented ASP.NET platform, a genome, and a support-ticket migration all yield to the same method.

04 Selected proof

Liquidity Services

2024 – present
Senior Product Owner, Platform Consolidation

Product owner and engineering bridge for a post-acquisition auction SaaS consolidation. Most of the work is deployment into operating clients.

  1. aBuilt the client migration program: a 7-phase process, an AI-automated per-client documentation pipeline, live cutover execution including full rollback and recovery, and a run-board playbook that turns every migration into institutional process.
  2. bDesigned and shipped a full client support platform migration under a hard vendor deadline: service design, SLA architecture, cross-project automation, an audited 6,600-ticket historical data migration with zero data loss, and the operations runbook, at zero incremental license cost.
  3. cRecovered a paused, at-risk client implementation and converted an 800+ hour requirements backlog into development-ready specifications covering 55+ user stories through an AI-assisted pipeline.
  4. dBuilt AI-augmented product operations as a discipline: six custom agentic skills in production, audited AI data pipelines, and multi-model validation practices.
  5. eContribute to platform security posture: WAF policy tuning, proactive threat detection through application monitoring, and cross-functional incident response.

jonmick.ai · AIs & Shine

2023 – present
Founder and sole builder

A live, publicly explorable personal AI operating system: 218 database tables, an 86-tool integration layer, semantic retrieval over 216,000+ embedded personal memories, a daily briefing agent, and a phone-callable voice agent. Designed, built, deployed, and operated by one person. I write about twice-exceptional cognition for 350+ subscribers at AI Gave Me Autism. AIs & Shine is pre-revenue by design: validating the method before scaling it.

The Director era

2021 – 2023
Yonder · Bazaarvoice

Built Yonder's product organization from zero to six PMs as the company's first product hire, then directed Bazaarvoice's data and analytics portfolio, used by half the company's enterprise clients, with a 40+ person organization. When generative AI arrived, the leverage moved from headcount to hands. Coming back in as a builder rather than a director was the deliberate part.

The Home Depot

2018 – 2020
Manager, Enterprise Product Management

Owned the Pro loyalty and customer programs platform serving millions of Pro members and roughly $20B in annual Pro sales, coordinating releases with Store Operations across 2,200 stores while retiring a legacy stack and integrating 15+ enterprise systems.

projekt202

2008 – 2012
UX Program and Product Manager

Embedded delivery for Charles Schwab, PayPal, Samsung, and Dell, from pitch through wireframes, implementation, QA, and renewal. The client relationship was the product.

Sagely

2023
Product Management Coach, embedded with Department of Defense teams

Designed a product management curriculum for DoD teams and coached Air Force product staff on the ground.

05 The full record

2024 – present Liquidity Services, Senior Product Owner, Platform Consolidation
2023 – present AIs & Shine, Founder. Pre-revenue by design, validating the method before scaling it
2023 Sagely, Product Management Coach embedded with DoD teams
2022 – 2023 Bazaarvoice, Director of Product Management
2021 – 2022 Yonder, Director of Product Management
2020 – 2021 Keller Williams, Senior Manager of Product Management, platform serving 180,000 associates
2018 – 2020 The Home Depot, Manager of Enterprise Product Management
2015 – 2018 Web.com, Senior Product Manager, internal platforms for 1,500+ employees
2013 – 2015 General Motors, IT Product Manager: enterprise BI portal, taught Scrum leadership classes, then product lead for GM's car-sharing pilot programs, later branded Maven
2008 – 2012 projekt202, UX Program and Product Manager
2005 – 2007 Deloitte Consulting, Consultant
Education
B.S. Business Administration, Arizona State University, full academic scholarship.
Also
Kripalu Certified Mindful Outdoor Guide and Forest Therapist, 100+ hours. Useful more often than expected.

06 What I'm looking for

The short version, in my own words: some kind of full-stack Design, Product, and Engineer role with a custom AI harness, where strategy and delivery stay in the same hands. The longer version has three parts.

The user I want to build for is the operator: the person inside an organization who has to live with the software every day, the planners, controllers, and crews who rarely get to choose their tools. That preference has run through every role since Deloitte. The pull toward the defense space never went away after coaching Air Force product teams. I still think about the Airmen.

The environment matters as much as the title: strong infrastructure and tooling, capable colleagues who work independently, complex technical and creative problems, small teams or high autonomy, low coordination overhead. I do my best work surrounded by people who are better than me at their thing.

Logistics: Austin-based and remote-first today, and willing to relocate for the right employer. The pull is toward the southern Appalachians; the forest-therapy certification was not a coincidence. I have worked at 100% travel (Deloitte) and 30% (Sagely); 25% is the ceiling I keep now. Eligible to obtain a security clearance; I entered the Top Secret investigation process in 2008, and it ended unfinalized when I changed jobs.

07 Honest footnotes

Drafted with my own AI infrastructure against a maintained claims ledger. The unverifiable was cut. The rest is me.

  1. iI build with AI models rather than training them. If the role is ML research, we will both be disappointed.
  2. iiI ran product organizations twice and returned to the work on purpose. A team of three to eight suits me and informal mentorship is where I am best; if the pitch is mostly headcount growth or coordination, someone else wants that more than I do.
  3. iiiAny claim in this document comes with provenance on request. That is a real offer and a fun conversation.
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