John Doe

AI/LLM Delivery & Governance Leader

LocationBoston, MA Emailjohn.doe@example.com Phone+1 (555) 010-0200 LinkedInhttps://www.linkedin.com/in/johndoe GitHubhttps://github.com/michaelljackson101 Portfoliohttps://johndoe.dev

Summary

  • Delivery leader focused on turning AI/LLM capability into auditable, repeatable outcomes through governance, operating models, and disciplined execution.
  • Builds Human-in-the-Loop workflows with traceability so teams can move fast without losing trust, control, or accountability.
  • Brings a practical “tooling + habits + metrics” approach to adoption (from pilots to scaled enablement), with measurable cycle-time improvements.

Skills

AI/ML
LLM workflow design, Prompting & evaluation, Human-in-the-loop patterns, Model risk awareness, NIST AI RMF literacy
Product
Narrative writing, Decision-grade synthesis, Stakeholder alignment, Metrics, Executive packaging
Engineering
Python, YAML-first authoring, Jinja templating, CLI tooling, Git workflows, Markdown-first artifacts
Leadership
Program governance, Operating model design, Change enablement, Delivery discipline, Risk/compliance communication

Education

State University
B.S., Computer Science
Jan 2012 — Jan 2016
  • Graduated with honors

Certifications

Certified Delivery Professional
Professional Services Training
2025
Cloud Architecture (Associate)
Cloud Provider
2021

Interests

Writing · Teaching · Systems thinking · AI literacy enablement

Experience

AI Transformation & Governance Lead
Global Consulting Firm — Boston, MA
Oct 2025 — Present
  • Authored an AI strategic plan with a Crawl–Walk–Run maturity model aligned to NIST AI RMF and practical delivery constraints.
  • Designed a TRACE-style methodology and TILETS learning loop for governed, auditable AI-assisted content production; achieved >60% workforce enablement and built an internal champion network.
  • Delivered 15+ AI assistants and reusable workflows; reduced manual reporting time ~40% and accelerated test design cycles ~30%.
  • Enabled rapid executive-ready packaging of complex workstreams by separating deterministic artifact generation from final narrative framing.
  • Built lightweight CLI automation and folder contracts so outputs were traceable, reproducible, and safe to reuse.
ai governance human-in-the-loop traceability windsurf
Builder — Intent-to-Artifact Workflows
Self-Directed (Open Work) — Remote
Oct 2025 — Present
  • Designed and implemented an “Intent Foundry” approach: staged human–AI collaboration that turns ambiguous intent into tempered, traceable, execution-ready artifacts.
  • Added recasting (forward intent→outputs and reverse outputs→evidence→inferred intent) to enable validation, auditability, and alternate stakeholder perspectives.
  • Standardized run packaging so any run can be re-issued as a single executive-ready document driven by run-scoped YAML specifications.
  • Practiced a tool-neutral AI co-collaboration model (e.g., Windsurf/Cascade) focused on durable artifacts, not chat transcripts.
intent trace yaml governed-ai windsurf
Senior Product Manager
Acme Software — Remote
Jan 2019 — May 2022
  • Owned roadmap for a workflow automation platform; grew active users 3x and improved retention by 18%.
  • Partnered with engineering to ship a reliability initiative; reduced Sev-1 incidents by 45% in two quarters.
  • Introduced lightweight experimentation and success metrics; accelerated decision-making and cut rework.
product platform

Projects

CVFoundry-Lite
Creator
  • Built a self-contained resume generator from canonical YAML + config YAML using Python + Jinja, optimized for portability (single HTML file).
  • Added validation + normalization so non-technical users can safely evolve a “CV source of truth” without template breakage.
  • Included build metadata (date/version/hash) to make outputs traceable and reproducible.
CVFoundry (Concept)
Designer
  • Developed a pragmatic approach to “CV as code”: structured data + templates + deterministic builds instead of manual Word/PDF version sprawl.
  • Used AI as a co-collaborator for refactors, not just drafting—keeping the canonical CV clean over time.
KOS / TRACE (Concept)
Author
  • Explored a tool-neutral control-plane for Human–AI co-collaboration where thinking becomes durable artifacts.
  • Applied traceability and human-in-the-loop endorsement to improve trust in AI-assisted knowledge work.
ResDoc Template + Shared Engines
Maintainer
  • Created a Markdown-first, Git-friendly scaffold for AI-assisted research and decision-support deliverables (TRACE loop + TILETS staging).
  • Promoted simple conventions (one artifact per run, deterministic naming) to improve reuse, governance, and team adoption.