I help organizations build and run data and AI platforms on their own infrastructure — reliably, securely, and without vendor lock-in.
Design and deployment of production AI platforms on your own infrastructure — from GPU provisioning to model serving and governance.
Typical engagements: platform readiness assessments, GPU cluster design and rollout on OpenShift/Kubernetes, model-serving pipelines, and the operational guardrails (monitoring, quotas, access control) to run AI workloads alongside everything else.
Self-hosted LLM deployments and retrieval-augmented generation pipelines that keep your data inside your perimeter — vector stores, ingestion, evaluation.
Typical engagements: model selection and on-prem serving, end-to-end RAG design (document ingestion, chunking, vector databases, retrieval quality evaluation), security hardening, and fully air-gapped deployments where required.
Cluster design, storage and operator strategy, and data pipelines — Spark, Kafka, and the modern data stack running reliably on OpenShift/Kubernetes.
Typical engagements: cluster architecture and installation, storage and operator strategy, migrations from legacy Hadoop stacks, streaming and batch pipeline builds, and log-analytics/SIEM platforms with full observability.
I take on a limited number of engagements at a time. Tell me about your project and I'll get back to you within 1–2 business days.
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