Private AI on Kubernetes

Private AI on Kubernetes — Self-Hosted Models and RAG Over Your Own Data

Run open-weight models and private RAG on your own cluster so sensitive data and prompts never leave your boundary.

Sending proprietary data and prompts to a third-party API is a non-starter for many teams. Private AI — self-hosted models and RAG on your own Kubernetes — keeps everything inside your boundary, but someone has to build and run it.

We help you build private AI on Kubernetes: serving open-weight models (Llama, Mistral, Qwen and others) with vLLM or KServe on your GPUs, private RAG over your own documents with your own vector store, embeddings that never leave your environment, workload identity and secret isolation, and the observability and cost controls to run it sustainably. Deploy on-prem, in your own cloud account, or hybrid — the point is that sensitive data, prompts, and outputs stay under your control. We help you match model size and hardware to real quality and latency needs rather than over-provisioning.

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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

Private AI Work We Help With

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

Serving open-weight LLMs (Llama, Mistral, Qwen) with vLLM/KServe on your own GPUs

Private RAG over your documents with an in-boundary vector store and embeddings

Network policy and egress control so no data or prompt leaves your boundary

Workload identity, secret isolation, and audit for sensitive AI

Right-sizing models and hardware to real quality and latency needs

Hybrid designs that keep the sensitive path private while scaling the rest

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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Running AI — model inference and often RAG over your own data — inside your own boundary (on-prem, your cloud account, or hybrid) so proprietary data, prompts, and outputs never go to a third-party API. On Kubernetes that means serving open-weight models with vLLM/KServe on your GPUs, a private vector store, and your own identity, security, and observability around it.

Open-weight models such as Llama, Mistral, Qwen, and others served with vLLM or KServe, sized to your quality and latency needs. For private RAG we help you choose embeddings and a vector store (pgvector, Milvus, Qdrant, OpenSearch) that run inside your boundary. We match model size to hardware so you are not paying for GPUs you do not need.

Network policy and egress control so inference and embedding traffic cannot reach the public internet, private endpoints for any managed components, secret isolation, and workload identity. We design the data flows explicitly and can add audit logging so you can prove nothing left.

Yes. Many teams keep sensitive inference and data private while using cloud for bursts or non-sensitive workloads. We design the split so the sensitive path stays private and the rest scales economically, with consistent tooling across both.

Message us on WhatsApp with your data-sensitivity needs, target models, and hardware/cloud. We will help you design or stabilise a private AI platform that meets your control and latency requirements — 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.