Trusted by teams at Chevron, Electronic Arts, HPE, and Sysco.
Credentials
PMPProject Management Professional
IBM AI EngineeringProfessional Certificate, completed
IBM RAG and Agentic AIProfessional Certificate
MPAPennsylvania State University
BS, Public Service LeadershipUniversity of Houston-Clear Lake
Titles vary. The work doesn't.
Job boards file this work under program manager, change manager, AI program lead, technical program manager, and forward-deployed or solutions roles. If you're matching on skills instead of titles, here is where I add the most value.
When the problem is still fuzzy. I sit with executives and customers until the real need is clear, then scope it and de-risk it.
Between the business and the builders. I translate in both directions, so engineers build the right thing and sponsors know what they're getting.
When adoption is the point. ADKAR-based change planning turns a launch into a habit, which is where most AI tooling programs stall.
When I need to prototype it myself. Python and AI tools let me put something in front of people early, instead of describing it.
Skills, with receipts
A list of strengths is easy to write. Here is what each one looked like on real work.
Project management
Eleven years running enterprise programs from intake to handoff, in agile, waterfall, and hybrid shops. I choose the method that fits the work, then keep scope, schedule, and risk visible to everyone who needs them.
Stakeholder management
Built working relationships with C-suite sponsors, Fortune 500 clients, and cross-functional teams, so hard tradeoffs get decided early instead of escalating late.
Change management
ADKAR-based adoption plans that treat people as the deliverable. I led an AI tooling change program at a large global software company, moving teams from pilot to everyday use.
Delivery and value
Turned requests into numbers: a self-service portal that cut fulfillment time by 73%, an online assessment platform for learning, and a value-creation plan for a large manufacturer.
AI enablement
I help organizations choose AI tools, set sensible guardrails, and teach people to use them well. I know how these tools work and where they break, which keeps the adoption plan honest.
Python and AI engineering
Building in public: a PM toolkit, a pipeline automation, and a survivor-support case study below. Completed IBM's AI Engineering Professional Certificate, and I'm applying it here to move toward forward-deployed roles.
Daily tools: Jira, Confluence, SharePoint, Teams, PowerPoint, Excel. Python as I build.
The forward-deployed toolkit
The tools forward-deployed and AI engineers reach for, stage by stage, with the spot where project management earns its keep. Filled dots are tools I've used in my own work. Hollow ones are on my map.
Find the real problem before anyone writes code. Forward-deployed work starts in the customer's world: their data, their workflow, and the person who feels the pain.
Jira tickets and delivery tracking (In my work)
Confluence discovery notes and decisions (In my work)
SQL query the customer's data (On my map)
Jupyter explore data fast (On my map)
Postman poke at customer APIs (On my map)
Where the plan matters. Stakeholder interviews, a success metric, and a risk list come before the first prototype.
Connect
Wire the model into the systems people already use. Most of the effort here is access, permissions, and plumbing, not AI.
Composio Gmail, Outlook, and Sheets connections (In my work)
MCP standard way to give models tools (On my map)
REST and webhooks move data between systems (On my map)
n8n and Zapier low-code glue (On my map)
OAuth and API keys access done safely (On my map)
Where the plan matters. Security review and data access are schedule risks. Name them in week one, with owners.
Build
Ship a first version that does one useful thing. Agents and chains come later, once the simple version has proved its worth.
Python my projects, end to end (In my work)
Claude assistant and coding partner (In my work)
LLM APIs Anthropic, OpenAI, and others (On my map)
LangChain and LangGraph chains and agent workflows (On my map)
FastAPI serve a model behind an endpoint (On my map)
Hugging Face and PyTorch models and training (On my map)
Where the plan matters. Scope the smallest slice that a real user would use, then protect it from additions.
Ground
Make answers come from the customer's own sources instead of the model's memory. Retrieval is where most enterprise assistants succeed or fail.
RAG retrieval-augmented generation (On my map)
Embeddings turn text into searchable vectors (On my map)
Chroma and FAISS local vector search (On my map)
pgvector and Pinecone vector search at scale (On my map)
Where the plan matters. Agree on the source of truth and who keeps it current. Stale documents make confident wrong answers.
Evaluate
Prove it works before anyone relies on it. A small test set and a clear pass mark beat a good demo.
Promptfoo and Ragas test prompts and retrieval (On my map)
LangSmith and Langfuse trace what the model did (On my map)
pytest test the code around the model (On my map)
Review rubric human acceptance checks (In my work)
Where the plan matters. Acceptance criteria, a go or no-go review, and a named owner for every failure.
Ship and adopt
Release it, then get it used. A launch that nobody changes a habit for is not finished.
GitHub version control and review (In my work)
Cloudflare Workers this site runs here (In my work)
ADKAR the adoption method I lead with (In my work)
Docker package it to run anywhere (On my map)
GitHub Actions automate tests and deploys (On my map)
AWS, Azure, and GCP the clouds customers run on (On my map)
Where the plan matters. Training, champions, and a feedback loop turn a release into a habit.
Things I'm building
PM Arsenal
Four tools a project manager reaches for every week, in one place: a Risk Scanner, an Executive Briefing Generator, a Capacity Tracker, and an AI Scenario Roleplay for practicing stakeholder situations.
In progress. I'm finishing the build on GitHub, then it goes live here.
A private place to document abuse, and a person to sit beside you in court
Next build. Shown as a case study for now: a concept, not a live service. Any demo uses made-up data.
For people leaving abusive situations who aren't ready to call the police. They log incidents with dates and times, assemble them into a packet for an investigator, get plain-language answers about the legal process, find free local support, and are matched with a trained volunteer to attend court with them.
Built for a shared or watched device. A quick-exit button, a neutral name and icon, and notifications that never reveal the purpose.
A log an investigator can trust. Entries are timestamped by the server, additions are allowed, and original entries keep their history.
A chatbot that stays in its lane. It explains the process from vetted sources, does not give legal advice, and surfaces hotline information whenever someone describes danger.
Volunteers as a safety program. Vetting, training, and a way to report problems come first, ideally with an existing advocacy organization.
Store less. Retention and what a court could compel are decided up front.
Proposed architecture
Neutral appPlain name and icon, quick-exit button
Private incident logServer timestamps, append-only history
Evidence packetAssembled for an investigator
Process assistantRetrieval over vetted sources, hotline shown when danger is described
Court buddiesVetted, trained volunteers, matched by request
If you need help now: National Domestic Violence Hotline, 1-800-799-7233 or thehotline.org.
Job-search pipeline automation
Connected Gmail, Outlook, and Google Sheets so applications, follow-ups, and fractional leads land in one tracker without copy and paste. A small build that shows how I think about workflows: find the manual step, then remove it.
Set up for my own search. A write-up is coming.
How it's wired
Gmail and OutlookWhere applications and replies arrive
ComposioConnects the inboxes and the sheet
Google Sheets trackerApplications, follow-ups, and leads in one place
Now building, in public
Real repositories, listed by most recent update, straight from GitHub.
Static pagesHTML, CSS, and a little JavaScript. No framework, self-hosted fonts
Cloudflare WorkersServes the site from the edge worldwide
Push to GitHubEvery change to main deploys automatically
An assistant, readyA Worker endpoint for a live Q&A chat is built and switched off for now
Common questions
What recruiters and hiring managers ask most, answered straight.
How does a project management background help in a forward-deployed role?
Forward-deployed work starts with a customer whose problem is only half described. I'm used to sitting with executives and operators, finding the real need, scoping it, and managing the risk. Then I can prototype in Python and AI tools, and get the people involved to use what we built. The combination of customer trust, delivery discipline, and technical fluency is the job.
Which companies have you supported?
Teams at Chevron, Electronic Arts, HPE, and Sysco have trusted me with their programs. I describe the work in general terms and don't tie any project, result, or tool to a specific client.
Which tools do you use, and which are you still learning?
In my own work: Python, Claude, Composio, Jira, Confluence, GitHub, Cloudflare Workers, SharePoint, Teams, PowerPoint, and Excel, plus ADKAR and human review rubrics for acceptance checks. On my learning map, not claimed as production experience: LangChain and LangGraph, Hugging Face and PyTorch, FastAPI, RAG patterns and vector stores, evaluation tools like Promptfoo and Ragas, Docker, GitHub Actions, and the major clouds. The toolkit section marks each one.
Why you for an AI program lead role?
I spent a year leading an AI tooling change program, moving teams from pilot to everyday use. That is the hard part of most AI programs, and it's a people problem more than a model problem. I also build, so I can judge scope and risk with engineers instead of relying on summaries.
How deep is your hands-on AI engineering?
Newer than my delivery and change experience, and I'd rather say so than hide it. I completed the IBM AI Engineering Professional Certificate and the IBM RAG and Agentic AI Professional Certificate, and I build projects in public so you can judge how I learn from the code, not a claim.
What's your path in short?
Eleven years in enterprise project and program delivery, growing into change management for Fortune 500 clients, then fractional consulting since August 2024. I most recently led an AI tooling adoption program at a large global software company, and I'm now moving toward director-level and hybrid PM and AI engineering roles.
Field notes
Adoption is the deliverable. A tool that nobody changes a habit for is shelfware. ADKAR keeps the plan honest about awareness, desire, knowledge, ability, and reinforcement, in that order.
Ask what the requester sees. A self-service path beat a faster team, because it removed the waiting instead of speeding it up.
Make risk boring. When risks are listed, owned, and reviewed on a schedule, they stop being surprises and start being line items.
Hiring for a role that sits between the plan and the build?