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.
Book a Strategy Call20+ years in enterprise software · Fortune 500 platform leadership · Dual M.S. in CS/AI & Engineering Management
100+
Engineer Organization Built & Led
$13M+
Secured in Program Funding
99.95%
Uptime in Production Environments
Up to 40%
Reduction in AI Infrastructure Costs
Capabilities
Core Practice Areas
Platform strategy, governance, and production execution — the work that gets AI systems shipped and keeps them running.
RAG System Design & Evaluation
Retrieval as context engineering—governing what enters the model's context at each step of an agent loop. Hybrid search, evaluation harnesses, and accuracy tied to business metrics.
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 satisfies security, legal, and audit on a predictable timeline.
AI-Assisted Engineering Enablement
Turning installed tools into delivery gains: the practitioner skill, working standards, and review discipline that keep AI-assisted delivery improving after the engagement ends.
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 taking AI from pilots to enterprise platforms
— Teams that need governance, security, reliability, and cost control before they can scale
— Engineering leaders whose teams have the AI tools and want the practice that turns 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 Success Looks Like
Outcomes at production scale.
Measured against where you start.
— Deployment cycles compressed from weeks to days
— 99.95% platform uptime across production workloads
— Up to 40% reduction in AI and cloud run-rate against pre-optimization spend
— 30–45% relative gain in retrieval recall@k over demo-grade baselines
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