Design and implement secure cloud pipelines that ingest very large scan datasets (multi-terabyte), reliably and resumably.
Build orchestration for GPU-accelerated reconstruction and analysis with strong retry semantics, idempotency, and cost controls.
Define end-to-end data lifecycle for medical imaging: raw vs intermediate vs derived artifacts, retention policies, and reproducibility.
Implement security + compliance primitives appropriate for HIPAA/PHI: encryption in transit/at rest, key management, least privilege, audit logs, and access reviews.
Build operational tooling: monitoring, alerting, runbooks, and incident-driven improvements for a growing device fleet.
Strong experience with cloud batch/queueing/orchestration, storage systems, and data pipeline reliability.
Experience shipping production systems that handle large data volumes and failure-prone networks.
Practical security mindset (least privilege, secrets, audit logging) and comfort operating in compliance-constrained environments.
Building reliable data pipelines at scale (queues/orchestration, resumable uploads, GPU batch execution) with strong observability.
Security + privacy by default: encryption, least-privilege access, auditing, and practical HIPAA/PHI guardrails.
Owning the “boring” backend details that keep a lean team moving: schemas/migrations, cost controls, retries, and runbooks.
Understanding compute tradeoffs across hardware options, and specifying appropriate cloud resources.
