Back to Clinical
Proof · Reliability & Pipeline Operations

A distributed environment, operated as a product.

Roughly 25 Proxmox lab nodes across application hosts, GPU workloads, databases, queues, and monitoring — operated with service separation, recoverability, queue-backed execution, and implementation discipline. The same approach transfers directly to long-running clinical pipelines, healthcare data ingestion, and EHR integration jobs that need real reliability rather than demo-grade scripts.

What the buyer gets

What the buyer gets
Failures stay recoverable
Batch checkpointing, queue-backed orchestration, fault tolerance treated as first-class concerns
Workloads scale without rewrites
GPU hosts, application services, message brokers, and data layers separated cleanly across nodes
Operations look like a product
Monitoring (Prometheus / Grafana / Loki), observability, and reliability are standing concerns, not afterthoughts
Service surface is reasoned about
Dedicated nodes for monitoring, control plane, application hosts, and data tiers — not one box doing everything
Implementation discipline ships
Operator-controlled changes, explicit phases, evidence capture, and signed decision records when behavior changes

Architecture notes

  • Roughly 25 Proxmox lab nodes operated as one coordinated environment
  • Specialized application hosts, databases, message brokers, monitoring, and GPU-backed workloads
  • Service separation across nodes, recoverability, workload placement, and queue-backed execution
  • Production-style pipelines with batching, checkpointing, orchestration, and fault tolerance

Capabilities demonstrated

Distributed systems operationsPipeline reliability designQueue-backed orchestrationBatch checkpointing and resumabilityService separation across nodesObservability (Prometheus / Grafana / Loki)Operator-controlled change discipline

Healthcare analog

The same operating posture applies to:

  • Long-running clinical pipeline orchestration
  • Healthcare data ingestion with retry and resume
  • EHR integration jobs that need recoverability
  • Production workflows that require monitoring rather than ad-hoc scripts

I do not run scripts on my laptop and call it a system. I operate distributed infrastructure with service separation, queue-backed orchestration, monitoring, and recoverability — the same operational concerns that matter when an AI workflow actually has to run in production.

Available for evaluation

Work with me to operate AI workflows in production

I take on workflow audits, AI implementation sprints, and fractional advisory through bounded scoped work.

Contact me