On-Prem AI Infrastructure Consulting
I help teams harden private AI operations for sensitive workloads: deployment boundaries, context controls, reliability guardrails, and healthcare-aligned integration workflows.
Prefer async? Send a message.
Production incident? Ask about 72‑hour stabilization.
Where I Focus
Architecture hardening for private AI systems: inference operations, context governance, DAG orchestration, and healthcare-aligned integration workflows.
Platform positioning
Define model hosting, tool access, and healthcare data flows before implementation.
Secure context (MCP)
Identity-aware access, audit logs, rate limits, and a safer boundary between agents and internal tools. Scope the gateway and role-based access controls against the deployment requirements.
Agent evaluation
Simulation-driven stress tests: adversarial inputs, tool misuse, and concurrency before it hits users.
GPU platform ops
Kubernetes + GitOps patterns for private/hybrid inference that is reliable, observable, and safer to deploy.
Engagement Models
Start with fixed-scope diagnostics, then move into build sprints or retainers once priorities are clear.
| Offer | Best for | Timeline | Price |
|---|---|---|---|
AI Infra Readiness AuditStart here A fixed-scope diagnostic: architecture review, cost model, risk register, and a prioritized execution plan. | Teams scaling inference, moving on-prem/hybrid, or trying to stop GPU spend from running away. | 2–3 weeks | $7.5k–$15k |
A two-week sprint to connect three agreed data sources or tools through the Model Context Protocol (MCP), with authentication, documentation, and an operational handoff. | Teams that built agents with point-to-point integrations and want a standard interface (MCP) instead. | 2 weeks | $15k–$50k |
Design and implement a centralized context gateway roadmap so agents can access tools as the user (OIDC), with audit logs, rate limits, and policy controls. | Teams that need governance planning and execution: identity-aware access, audit trails, and a safer boundary between agents and internal systems. | 4–8 weeks | $40k–$150k+ |
I run load tests, inject adversarial inputs, and profile cost and latency—so you find limits before users do. | Teams with agents that “work in demos” but fail under load, weird inputs, or real operational constraints. | 2 weeks | $20k–$60k |
Design and implement a GPU platform with defined operating requirements with observability, GitOps, and safe deployment workflows. | Teams that need a GPU platform with dashboards, runbooks, and alert patterns—with documented ownership. | 4–10 weeks | $25k–$100k+ |
72 hours of focused work: identify what broke, patch it safely, and leave you with dashboards for the next issue. | Production incidents, GPU platform instability, or release blocks where you need experienced hands fast. | 72 hours | Fixed fee (quote) |
Stabilize training/inference pipelines and the systems around them: incidents, SLAs, and release hygiene. | Teams with pipelines that fail unpredictably, slow incident response, or surprises under load. | 2–4 weeks | Project-based |
Senior guidance and execution support: roadmap, vendor selection, and operational decision-making. | Teams that need senior infrastructure leadership without a full-time hire. | Monthly | $6k–$20k/mo |
AI Infra Readiness Audit
A fixed-scope diagnostic: architecture review, cost model, risk register, and a prioritized execution plan.
Teams scaling inference, moving on-prem/hybrid, or trying to stop GPU spend from running away.
- A 60-minute architecture walkthrough (current state + constraints)
- A rough GPU cost breakdown (or billing export) for the last 30–90 days
- Top incidents / reliability pain points (even if it’s informal)
- Access to CI/CD + GitOps flow (repos, environments, rollout process)
- A plan you can execute (roadmap + sequencing)
- A cost baseline (cloud vs private/hybrid TCO)
- A reliability plan (SLIs/SLOs, incident response, runbooks)
- Scorecard across cost, reliability, security, operability
- Written architecture review + recommendations
- GPU cost model + scaling assumptions
- Risk register + mitigation plan
- 90-day roadmap with sequencing + owners
MCP Integration Sprint
A two-week sprint to connect three agreed data sources or tools through the Model Context Protocol (MCP), with authentication, documentation, and an operational handoff.
Teams that built agents with point-to-point integrations and want a standard interface (MCP) instead.
- A prioritized list of 3 data sources/tools (DBs, docs, internal APIs)
- Access details + security constraints (SSO/OIDC, network boundaries)
- Expected usage model (local-only, centralized gateway, or both)
- Assistants can query real internal context without copy/paste workflows
- A repeatable pattern for adding the next integration
- Clear security boundaries (who can access what, and how it’s audited)
- Custom MCP servers (Go or Python) for the selected sources
- Deployment plan (local, K8s, or hybrid) with configs and docs
- Operational runbook + ownership handoff
Secure Enterprise Context Gateway (MCP)
Design and implement a centralized context gateway roadmap so agents can access tools as the user (OIDC), with audit logs, rate limits, and policy controls.
Teams that need governance planning and execution: identity-aware access, audit trails, and a safer boundary between agents and internal systems.
- IdP details (OIDC/OAuth), role model, and required audit fields
- Current MCP servers (if any) + target tools/data to broker
- Where it will run (K8s/on-prem/cloud) and network constraints
- Agents access tools with least privilege (not shared super-admin keys)
- Auditable context access (“who asked what, when, on behalf of whom”)
- Controlled blast radius (rate limits, policies, and safe defaults)
- Gateway deployment + configuration (routing, authn/authz, policy hooks)
- Audit logging pipeline + dashboards for usage and failure modes
- Hardening checklist + threat model focused on context exfiltration
The Agent Stress Test
I run load tests, inject adversarial inputs, and profile cost and latency—so you find limits before users do.
Teams with agents that “work in demos” but fail under load, weird inputs, or real operational constraints.
- A testable agent surface (API, UI, or CLI) plus representative tasks
- Success criteria (accuracy, latency, cost, safety), even if rough
- Access to telemetry (logs/metrics/traces) or exports for analysis
- A report of observed failures and their conditions
- Fewer surprises from prompt injection, tool misuse, and concurrency
- A baseline you can re-run before releases
- Evaluation harness + scenarios (including adversarial cases)
- Report: top failures, root causes, and prioritized fixes
- Recommended guardrails (timeouts, retries, budgets, tool policies)
GPU Cluster Design & Build
Design and implement a GPU platform with defined operating requirements with observability, GitOps, and safe deployment workflows.
Teams that need a GPU platform with dashboards, runbooks, and alert patterns—with documented ownership.
- Repeatable environments (dev/stage/prod)
- Safer deployments (GitOps, rollout strategies, rollback paths)
- Operational visibility (metrics/logs/traces + alerts)
- Reference architecture + implementation plan
- GitOps repo structure + CI/CD patterns
- Dashboards, alerts, and runbooks
72‑Hour Stabilization (Incident Response)
72 hours of focused work: identify what broke, patch it safely, and leave you with dashboards for the next issue.
Production incidents, GPU platform instability, or release blocks where you need experienced hands fast.
- Fast triage and containment
- Improved time-to-diagnosis via targeted instrumentation
- A written “what happened / what we changed / what’s next” handoff
- Triage plan + incident timeline
- Immediate fixes (safe, reversible)
- Stabilization backlog + ownership recommendations
MLOps Reliability Sprint
Stabilize training/inference pipelines and the systems around them: incidents, SLAs, and release hygiene.
Teams with pipelines that fail unpredictably, slow incident response, or surprises under load.
- Faster time-to-diagnosis and fewer repeat incidents
- Better deploy safety (gates, canaries, error budgets)
- Clear ownership and escalation paths
- SLOs + alerting tuned to your workload
- On-call playbooks + incident templates
- Implementation PRs (where appropriate)
Fractional AI Infra Lead (Retainer)
Senior guidance and execution support: roadmap, vendor selection, and operational decision-making.
Teams that need senior infrastructure leadership without a full-time hire.
- Clear priorities and sequencing
- Less tooling churn and more operational signal
- A plan to hire/hand off sustainably
- Weekly touchpoints + async support
- Decision logs + architecture reviews
- Hiring plan + onboarding for future owners
Start with the Readiness Audit
If you want a concrete plan before you commit to a build, start here.
Example artifacts
I optimize for written outputs you can hand to a team: what’s broken (and why), what to do next, and how to verify progress.
See the Work
I publish the work: demos, write-ups, and implementation details you can evaluate before we talk.
How Engagements Work
Clear scope, measurable outcomes, and documentation your team can keep using.
Step 1
Fit call
15–30 minutes to confirm goals, constraints, and whether we can get a meaningful win.
Step 2
Paid diagnostic
Fixed-scope audit with written outputs (architecture, cost model, risks, roadmap).
Step 3
Build + stabilize
Implement changes in a safe sequence: observability → reliability → cost/scale.
Step 4
Handoff / retainer
Document runbooks and ownership, then decide whether ongoing support is valuable.
FAQ
Most engagements start with the Readiness Audit. It produces a written operational readiness baseline and de-risks implementation.
Yes. I can build new MCP servers (Go or Python), harden existing ones, and deploy them locally or in Kubernetes with a clean handoff.
Yes. That’s a common blocker for enterprise use. We can scope and implement a context gateway roadmap so agents access tools as the user (OIDC), with logs and rate limits.
No. I focus on operational readiness: architecture boundaries, traceability, and controls your compliance team can evaluate.
No. I work across cloud, hybrid, and on-prem. The goal is predictable performance, cost, and operations.
Implementation is project-based with clear deliverables and milestones. If you need flexibility, a retainer can work better.