OpenShift AI — Enterprise & Hybrid

OpenShift AI Job Support — Enterprise, Hybrid & Private AI on Red Hat OpenShift

Red Hat OpenShift AI for regulated, hybrid, and on-prem AI: distributed inference with llm-d, KServe/vLLM serving, Llama Stack, GPU, and the governance enterprises need.

Running AI on OpenShift means enterprise expectations — RBAC, GitOps, observability, air-gapped delivery — layered over the fast-moving world of vLLM, KServe, llm-d, and GPU operators. That is a lot to get right.

We help you operate Red Hat OpenShift AI across the stack: distributed inference with llm-d (Kubernetes-native distributed serving on vLLM, available as Technology Preview in recent OpenShift AI releases) deployed via KServe, KServe/LLMInferenceService and vLLM for standard serving, the Llama Stack Operator (LlamaStackDistribution) for agentic/GenAI building blocks, the NVIDIA GPU Operator, MCP servers, RBAC and service-mesh routing, GitOps configuration, and full observability with pre-built dashboards. OpenShift AI shines for hybrid, regulated, and sovereign workloads — and we also run the Red Hat AI Inference Server (vLLM) in air-gapped/disconnected clusters. We advise honestly on OpenShift vs the hyperscaler managed services for your constraints.

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What We Offer

Expert Support for Every IT Challenge

From daily job support to emergency production fixes, proxy interview guidance, and interview coaching — we have the expert for your specific need.

Real-Time Kubernetes AI Job Support

Live expert help during your working hours — running LLM inference (vLLM, KServe, Dynamo), agent runtimes and sandboxes, GPU scheduling, autoscaling, RAG pipelines, and daily platform deliverables on your real cluster so you always hit your deadlines.

Production AI Incident Support

On-call firefighting for live incidents — GPU Pods stuck Pending, CUDA/OOMKilled crashes, vLLM out-of-memory, high TTFT, model-loading failures, autoscaling that will not scale, agent loops, MCP authorization errors, and RAG/vector-DB latency — with an engineer on the call.

Interview & Candidate Marketing

Kubernetes AI proxy interview assistance, profile positioning, and candidate marketing for Platform Engineer, AI Infrastructure Engineer, GPU Infrastructure Engineer, MLOps/LLMOps, and SRE roles — real-time interview guidance, recruiter readiness, and profile visibility.

Real Situations

OpenShift AI Work We Help With

These are the real-world situations our experts resolve every day — for job support and interview assistance.

Serving LLMs with KServe/LLMInferenceService and vLLM on OpenShift with autoscaling and safe rollouts

Piloting llm-d distributed inference (Technology Preview) with service-mesh routing between workers

Deploying the Llama Stack Operator for agentic and GenAI building blocks

Configuring the NVIDIA GPU Operator and GPU scheduling on OpenShift

Running air-gapped/disconnected AI with the Red Hat AI Inference Server (vLLM)

RBAC, GitOps, and observability for regulated enterprise AI platforms

Global Reach

Real-time Kubernetes AI infrastructure support for engineers across USA, Canada, UK, Ireland, Germany, Netherlands, Switzerland, Australia, New Zealand, Singapore, UAE, and worldwide.

Available across US, Canada, UK, European, Australian, and Asia-Pacific business hours — and 24/7 for production incidents.

In-house experts — no sub-contracting or outsourcing

24/7 availability for urgent job support and interview needs

Confidential & professional — NDA available on request

Same-day onboarding for most job support and interview cases

Combined job support + proxy interview service available

Expert Help Available

Need real-time IT job support or interview help? Our experts are available 24/7 — USA, Canada, UK, Europe & worldwide.

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FAQ

Frequently Asked Questions

Everything you need to know before getting started with job support or interview assistance.

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Model serving with KServe and vLLM, distributed inference with llm-d (Technology Preview in recent OpenShift AI versions, deployed through KServe with service-mesh routing between workers), the Llama Stack Operator for agentic/GenAI building blocks, the NVIDIA GPU Operator and GPU scheduling, MCP servers, RBAC and security, GitOps and pipelines, observability, and hybrid/air-gapped deployment — on OpenShift and OpenShift AI Self-Managed.

llm-d is a Kubernetes-native distributed LLM serving stack built on vLLM, launched in 2025 by Red Hat with Google Cloud, IBM Research, NVIDIA, and CoreWeave. On Red Hat OpenShift AI it is offered as a Technology Preview (for example llm-d v0.2 in OpenShift AI 2.25), unified through KServe (the LLMInference CRD) with service-mesh routing, dashboards, and GitOps. We are explicit about Technology-Preview status and help you pilot it safely rather than assume GA.

Yes. We help you run AI in fully disconnected OpenShift clusters — the Red Hat AI Inference Server (vLLM) supports air-gapped deployment and benchmarking — with internal registries, model mirroring, data-residency-aware architecture, RBAC, and audit. This is a core strength for financial services, healthcare, pharma, government, and other regulated sectors.

OpenShift AI fits when you need hybrid/on-prem, data sovereignty, air-gapped operation, consistent tooling across clouds and datacenters, and enterprise governance. A hyperscaler managed service may be simpler when you are all-in on one cloud and do not need that control. We help you choose on the merits and connect them where you run both.

Message us on WhatsApp with your OpenShift version, what you are deploying (serving, llm-d, Llama Stack, GPU), and your constraints (hybrid, air-gapped, regulated). We will work it with you same-day.

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Real developers. Real solutions. Job support and proxy interview assistance available 24/7 across USA, Canada, UK, Europe, Australia, Germany, Singapore, and New Zealand.