Integration Engineer, Clinical Data Path

a2z Radiology AI is focused on building clinical AI that enhances the work of radiologists. They are seeking an Integration Engineer to manage the clinical data path, ensuring seamless integration from hospital systems to their AI platform.

Responsibilities:

• On-premise DICOM ingest. A pynetdicom C-STORE service that assembles studies, handles senders that do not signal completion cleanly, applies backpressure, passes compressed transfer syntaxes through, and spools safely when the link drops
• Cloud ingest. A DICOMweb STOW-RS receiver with bounded-memory multipart parsing, study-UID cross-checking, and exactly-once downstream firing per request
• Tenancy and privacy at the boundary. Customer resolution with no silent default, DICOM PS3.15 confidentiality rules, fail-closed checks before data leaves the customer network, and a re-identification store under customer-held keys
• The outbound lanes. JSON callbacks, dictation-system autotext, and HL7 v2 ORU^R01 over MLLP, shaped to the interface engine the customer actually runs
• Visibility and correctness. Per-study tracing, three-component integration health, configuration-drift checks, and CI for both gateway repositories in week one
• The technical truth in partner review. Precise answers on transport, retention, incident handling, and what the system does and does not do

Requirements:

• You have implemented a protocol from its specification, not only called a library that wrapped one
• You have run software inside infrastructure you could not SSH into
• A bare 502 with no application log makes you widen the system boundary, not add another log line and hope
• Fail-closed is your instinct when patient data is involved
• You can write a document a hospital IT reviewer will trust because it is precise, traceable, and honest
• Protocol literacy, outside-in debugging, and fail-closed instincts
• DICOM, PACS, VNA, or teleradiology systems
• HL7 interface engines
• pynetdicom, pydicom, Orthanc, dcm4che, or DCMTK
• networking, embedded, or payments systems
• AWS and Terraform
• healthcare or another regulated environment
• de-identification or privacy engineering
• confidential computing or hardware attestation