About
I work in the gap between strategy and delivery
My work is to make sure strategy survives contact with reality. I define the problem clearly, align the teams that need to solve it, and help execution hold together across customers, product, engineering, and operations. I have done that building customer-facing organizations from scratch, scaling revenue from $30M to $100M+ ARR, and leading teams through complex change.
Scale
$100M+
ARR built and scaled
Leadership
10 to 50+
FTE teams led across growth stages
Foundation
The business foundation
20+ years of building, hiring, and leading customer-facing organizations. That is the foundation under everything else here.
Building from 0→1 (and scaling it)
Built Sales, Pre-Sales, and Customer Success from zero at OceanX. Scaled to $100M+ ARR. Defined roles, hired teams, set standards, created the operating rhythms.
Leading people through change
Highest manager score company-wide at Guthy|Renker. Led post-acquisition integration at Tribune. Managed teams from 10 to 50+ FTEs plus offshore support. I know how to bring people along — not just deploy technology at them.
Technical credibility
BS Engineering (USC). Architected cloud-native SaaS platform (AWS, PCI L1). Led technical integrations, API implementations, architecture reviews. I can sit in a room with engineers and add value.
Customer-facing + strategic
Enterprise discovery, pre-sales scoping, C-level negotiations, strategic account management. Translate customer needs into product direction and protect the team from chaos.
Role Fit
How to think about my role
I operate at the system level: business objectives, customer needs, technology constraints, organizational dynamics, and the people who have to make the work real. Sometimes that means hiring the first ten people and defining the roles. Sometimes it means leading a larger cross-functional organization. Increasingly, it also means deciding where AI belongs in the workflow and where human judgment still matters most.
Where I add value
- Ensure that execution is anchored to a strategy that holds up — against product, customers, competition, and market reality
- Define the real problem before teams overbuild or optimize the wrong thing
- Ensure clarity between leadership, customers, product, and engineering without losing signal
- Design the operating model: roles, handoffs, review rhythms, escalation paths, and standards
- Lead through ambiguity, resistance, and organizational change without stalling execution
- Evaluate technical trade-offs well enough to keep quality, security, usability, and delivery aligned
AI Layer
AI as an accelerant
I completed Stanford's AI Professional Program — four graduate-level certificates in ML, NLP, and Computer Vision. The point was not collecting credentials. It was building enough technical fluency to separate signal from hype and apply the tools to real business problems.
I use AI the same way I approach any operating problem: define the objective, understand the workflow, evaluate the architecture, and make sure the system is useful in practice. The value is not in novelty. It is in choosing where AI creates leverage, where guardrails are needed, and how adoption actually happens inside a team.
What this looks like in practice
- Built production websites using AI-augmented development workflows
- Designed and built RAG systems, ML pipelines, and multi-agent prototypes
- Write analytical briefs that turn complex AI and business topics into clear operating decisions
- Evaluate AI architectures, retrieval approaches, model selection, and security trade-offs
Why It Works
Why this combination is useful
Many business leaders talk about AI at a high level but cannot engage the workflow, architecture, or delivery details. Many technical people understand the systems but have not spent years building revenue organizations, running strategic accounts, or leading teams through organizational friction. The value of my background is that I can hold both conversations at once.
I've led organizations through exactly the kind of hard change that AI adoption demands — post-acquisition integrations, building functions from zero, scaling teams through ambiguity and resistance. Teams ranging from 10 to 50+ people, highest manager score company-wide, and a track record of creating environments where strong people do their best work. That foundation — combined with real AI technical fluency and the ability to build trust through uncertainty — is what positions me to lead this next wave.
Selected Work
Selected work
A few representative examples of the pattern in my work: clarify the problem, align the teams, work effectively with technical stakeholders, and deliver outcomes that hold up in the business.
Case Study 1
Scaling an enterprise SaaS business from $30M to $100M+ ARR
Context
The company needed to scale revenue and customer delivery at the same time. That required more than selling. It required building the customer-facing operating system: roles, handoffs, standards, strategic account coverage, and a tighter link to product and engineering.
What I did
Built Sales, Pre-Sales, and Customer Success from zero. Hired and developed teams, led strategic accounts, ran technical discovery and solution scoping, and turned field feedback into product direction and operating cadence.
Outcome
Scaled the platform from roughly $30M to $100M+ ARR with 30%+ YoY growth for three consecutive years. Improved the company's ability to sell, deliver, and retain enterprise customers at higher levels of complexity.
Why it matters now
It is the same pattern companies face in AI transformation: multiple teams, real delivery risk, executive expectations, and technical detail that matters.
Case Study 2
Architecting the platform and technical delivery behind enterprise commerce
Context
Before the commercial organization could scale, the platform itself had to support enterprise customers: cloud architecture, integrations, onboarding patterns, delivery quality, and a cleaner path from technical discovery to customer value.
What I did
Built and led the technical e-commerce team from scratch, owned platform architecture and customer delivery, led integrations and API implementations, and created templates and playbooks that made delivery more repeatable.
Outcome
Helped ship a cloud-native AWS subscription commerce platform, supported PCI Level 1 requirements, accelerated customer time-to-value, and reduced cart abandonment by 25% through better architecture and delivery patterns.
Why it matters now
This is the technical layer behind my operator profile: I can work inside architecture, integration, and delivery detail while keeping the business objective in view.
Case Study 3
Applied AI: from coursework to practical systems
Context
I wanted recent, practical fluency in AI, not just executive-level familiarity. That meant formal study plus hands-on work building small systems, evaluating trade-offs, and understanding where AI fits in real workflows.
What I did
Completed Stanford graduate coursework in ML, NLP, and Computer Vision. Built RAG prototypes, multi-agent workflows, and evaluation-oriented experiments using Python, PyTorch, and LLM APIs. Used AI-assisted tooling to ship production websites and test practical delivery patterns.
Outcome
Developed enough technical fluency to evaluate architecture choices, spot weak assumptions, and translate business problems into workflow, data, and AI designs. The result is not just theory; it is an operator's working understanding of how these systems behave.
Why it matters now
My value is in connecting the strategy conversation to workflow design, technical judgment, governance, and adoption.
Open To
What I'm looking for
Roles where I can connect strategy, technical teams, customer reality, and execution. Best fit is Revenue / GTM leadership with technical depth, AI transformation, Head of Solutions, or strategic customer leadership where the challenge is making the system work end to end.
California-based or fully remote. Also open to interim or fractional engagements where the need is immediate operating leverage.