We build the infrastructure AI runs on.
Most AI never leaves the notebook. We close the gap between a model that works on a laptop and a platform that serves it reliably, at scale, and at a cost you can defend.
| GPU platforms | GPU scheduling and orchestration on Kubernetes, MIG partitioning, node affinity and taints, capacity planning, utilization tuning. Idle GPUs are the largest line item in most AI budgets. |
| Multi-tenant Kubernetes | Control planes (Kamaji), per-tenant provisioning and automated teardown, network policy and isolation, MetalLB / K3s / RKE2 / Rancher, bare-metal and air-gapped builds for data-sovereign environments. |
| MLOps pipelines | Ray for distributed training and inference, Kubeflow, MLflow, Airflow, model serving, feature stores, cost-aware routing between model tiers. |
| LLM and agent systems | Retrieval pipelines, evaluation harnesses, tool and MCP server design, token budgets, and agents that operate real infrastructure behind approval gates. |
| Reliability and cost | Prometheus, Grafana, Loki, OpenTelemetry, SLOs, incident response, FinOps. We have cut cloud spend by 60%+ on a live real-time platform without touching headroom. |
Short, senior, hands-on engagements. We plan before we apply, we show the diff, and we leave behind the runbook and the guardrail rather than a dependency on us.
Orchestration
AI and MLOps
Cloud
Observability
Languages
| Where | Link |
|---|---|
| Website | eprecisio.com |
| linkedin.com/company/eprecisio | |
| X | @EprecisioTech |
| @eprecisiotech | |
| EprecisioTechnologies | |
| ehtisham@eprecisio.com | |
| Location | Texas, United States |
Led by Ehtisham Mubarik, Senior Platform and AI Infrastructure Engineer. CKA and CKAD certified.