LLMOps Platform Architecture
The platform layer under your production AI: deployment, evaluation, prompt and dataset governance, and lifecycle management designed as one system your teams ship on.
Fractional VP of AI Enterprise AI Platforms · Agentic Systems · AI Governance
Scale AI from pilot to production.
I build the platform, governance, and operating model that gets AI past the pilot.
Start with the AI Platform Diagnostic20+ years in enterprise software · Fortune 500 platform leadership · Dual M.S. in CS/AI & Engineering Management
100+
Engineer Organization Built & Led
99.95%
Uptime in Production Environments
4 days
Release Cycle, Down from 2+ Weeks
Capabilities
Core Practice Areas
Platform strategy, governance, and production execution — the work that gets AI systems shipped and keeps them running.
AI Agents & Orchestration
Agent systems that coordinate tools and workflows inside explicit safety, permission, and audit boundaries, with the observability to debug them in production.
AI Governance & Compliance
Governance that accelerates deployment rather than gating it: a defined path from proposal to production that gives security, legal, and audit the evidence they need on a predictable timeline.
Capability Transfer & Interim Leadership
Engagements scoped to end: the AI or data leadership seat filled now, and the platform, the evaluation discipline, and the team to run them handed to permanent internal ownership.
Who I Work With
Organizations ready to move beyond experimentation.
For teams past the pilot and short the platform leadership to scale it.
— CTOs and VPs of Engineering whose AI pilots work and have not reached production
— Organizations where AI governance and run-rate have started to bite, with no one owning either
— Engineering teams already running coding agents, without the standards and review discipline to turn them into delivery gains
— Organizations hiring a fractional VP of AI, not another consulting deck
Why Teams Call
What I hear before an engagement starts.
- “We have working pilots, but no platform strategy to scale them.”
- “We're running agents in production but can't tell when they're quietly failing.”
- “We're getting bills for AI tool-use but can't tell what's driving cost or value.”
All three are platform problems before they are model problems.
What You Can Expect
Production outcomes, not pilot demos.
Every engagement starts by measuring where you are, so the change is on the record.
— Release cycles cut from weeks to days — I took HP's from 2+ weeks to under 4
— Platforms engineered for 99.95% uptime, the standard I held across 50+ production models
— AI spend traced to the agent, workflow, and tool call that caused it, then cut where it buys nothing
— Retrieval proven against a versioned test set before it ships — the method that took my own reference system from 58% to 100% hit rate
Let's Talk
Ready to turn AI investments into production systems?
Bring the initiative and the constraint. We'll scope it in 30 minutes.
Get in Touch