Operationalizing Generative AI at Scale
25 years of IT Architecture expertise focused on the hardest infrastructure challenge today: transforming experimental AI models into production-ready platforms on Kubernetes.
Expertise
Cloud Native AI Platform Stack
Building production-ready AI infrastructure using cutting-edge Cloud Native technologies
Model Serving & Inference
KServe for standardized model deployment, autoscaling, and multi-framework support (TensorFlow, PyTorch, vLLM, Hugging Face).
API Gateway & Traffic Management
Gateway API with Inference Extensions for intelligent routing, load balancing, and request orchestration across AI endpoints.
AI Agent Orchestration
KAgent and Model Context Protocol (MCP) for Kubernetes-native control plane and governance of intelligent AI workloads.
Platform Engineering
Internal Developer Platforms (IDP) with ArgoCD, Crossplane, Backstage, and GitOps workflows for self-service AI infrastructure.
Observability
Grafana, and OpenTelemetry for end-to-end ML lifecycle management and monitoring.
Security & Compliance
Istio service mesh, Kyverno policies, OPA/Gatekeeper, and external-secrets for enterprise-grade governance and compliance.
The Challenge
Platform Engineering for Kubernetes-Native AI
Building Platform Layers, Not Just AI Deployments
The difference between a scattered AI infrastructure and a production-grade platform is intentional architecture. I design the specialized platform layer that enables Kubernetes to serve as the control plane for your entire AI ecosystem.Standardized Workload Management
Move beyond ad-hoc AI deployments to standardized Kubernetes workloads with proper resource management, autoscaling, and lifecycle governance.
Enterprise Security & Compliance
Implement zero-trust networking, RBAC, network policies, and compliance controls without vendor lock-in using open standards (Istio, Kyverno, OPA).
Multi-Tenancy & Isolation
Design platform layers that enable multiple teams and workloads to coexist safely, with proper resource quotas, namespace isolation, and governance.
Open Standards, Not Vendor Lock-In
Build on Kubernetes-native patterns (KServe, Gateway API, MCP) that remain portable across cloud providers and on-premises infrastructure.
Hands-On Implementation
Assess & Diagnose
Deep-dive into existing infrastructure, identify bottlenecks, and understand real problems blocking production AI deployment.
Build & Fix
Roll up sleeves and implement solutions directly—from Kubernetes manifests to CI/CD pipelines. No handoff, just hands-on execution.
Deploy & Validate
Get AI models running in production. Troubleshoot issues in real-time, optimize performance, and ensure reliability under load.
Transfer Knowledge
Document what was built, why it works, and how to maintain it. Enable teams to operate independently after engagement.
Value Proposition
Why Choose Kubekub
The discipline of a seasoned architect applied to the innovation of AI
Proven experience with Fortune 500 enterprises: Roche, Hexagon, European Commission.
CKA, CKAD certified. Google Cloud and AWS certified architect.
Specialized in KServe, Gateway API Inference Extension, KAgent, and KMCP.
Part-time, short-term strategic contracts designed for high-impact results.
Transform experimental models into compliant, production-ready AI services.
Fractional AI platform leadership to guide your long-term platform strategy.
FAQs
Frequently Asked Questions
Common questions about working with Kubekub
What is Cloud Native AI Platform Engineering?
It's the specialized practice of building production-grade infrastructure for AI/ML workloads on Kubernetes. This includes model serving, autoscaling, traffic management, governance, and compliance—transforming experimental AI into enterprise-ready services.
What types of engagements do you accept?
I focus on short-term, high-impact strategic engagements on a part-time basis. This includes platform architecture design, infrastructure implementation, team enablement, and fractional leadership for AI platform strategy.
Do you work with startups or only enterprises?
I work with organizations at any stage who are serious about operationalizing AI at scale. Whether you're a startup building your first production AI platform or an enterprise modernizing existing ML infrastructure, I can help.
What makes your approach different?
I bring 25 years of IT architecture discipline to the rapidly evolving AI space. Rather than just deploying tools, I architect the specialized layer that enables data science teams to succeed—focusing on standards like KServe, Gateway API, and Kubernetes-native patterns.
What certifications and experience do you have?
CKA (Certified Kubernetes Administrator), CKAD (Certified Kubernetes Application Developer), Google Cloud certified, AWS certified. 25 years in IT Architecture with 10+ years focused on Cloud Native platforms. Proven track record with Roche, Hexagon, and European Commission.
How do I get started?
Reach out via the contact form to schedule an initial consultation. We'll discuss your AI platform challenges, current infrastructure, and goals. From there, I'll propose a tailored engagement approach.
Ready to Transform Your AI Platform?
Looking for a fractional leader to set your AI platform strategy? Let's connect and discuss how Kubekub can accelerate your journey to production-ready AI infrastructure.