> Markdown version of [/jobs/ext/2581157-integration-engineer-clinical-data-path](https://www.wearedevelopers.com/jobs/ext/2581157-integration-engineer-clinical-data-path). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Integration Engineer, Clinical Data Path - **Company:** A2Z RADIOLOGY AI INC. - **Location:** Boston, MA, United States - **Salary:** $140,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Clinical Data Repository, Cloud Computing, Continuous Integration, Dicom, JSON, Log Files, Packet Analyzer, Health Level Seven International - **Published:** August 9, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=d7ec75a5c6bdf5b8 ## About the Role Someone who has implemented a protocol straight from its specification, not just used a library that wrapped one, because DICOM and HL7 are old, strange, and written in prose you have to read carefully and get right. Someone who has run software inside infrastructure they couldn't SSH into, where the only feedback is a log file a stranger emails you. When a bare 502 shows up with nothing in the application logs, you reach for a packet capture or a memory profile before you reach for another log line. You fail closed by instinct when patient data is involved, and you can write a document a hospital IT reviewer will trust because it's precise and honest about what the system does and doesn't do. ## Description Getting a study from a hospital's systems into ours, running our model, and getting the result back is where a lot of clinical AI quietly fails. A study arrives incomplete. A sender never closes its association. A parser reads a whole request body into memory and the task disappears behind a bare 502. A firewall rule meant for one address gets applied to the wrong egress IP and produces a mid-stream 403 that looks exactly like a TLS failure. This role owns that path end to end: how studies come in, how results go back, and how we show a hospital's IT team that it all works. We build clinical AI that reads alongside radiologists. Our abdomen-pelvis CT triage device is FDA-cleared, and it's the first commercial system to simultaneously triage seven urgent conditions on abdomen-pelvis CT in the U.S. We're backed by Khosla Ventures. A cleared device only helps a hospital once it works inside the systems a radiologist already uses. Our clinical data path covers software running inside hospital networks, cloud DICOMweb ingest, de-identification, tenancy, result delivery, and the documentation an IT reviewer expects. It is owned by one engineer today and needs a dedicated owner, and hardening the path, including bringing the gateway repositories under proper continuous integration, is early work for whoever takes it on. What you'd own On-premise ingest. A pynetdicom C-STORE service that assembles studies, copes with senders that never signal completion cleanly, applies backpressure, passes compressed transfer syntaxes through untouched, and spools to disk so nothing is lost when the link drops. Cloud ingest. A DICOMweb STOW-RS receiver with a streaming, bounded-memory multipart parser, per-part study-UID checks, and exactly one canonicalization and one inference firing per request. Tenancy and privacy at the boundary. Working out which customer a study belongs to, with no silent default, applying the DICOM PS3.15 confidentiality rules, running a fail-closed check before anything leaves the customer's network, and keeping a re-identification store encrypted under a key the customer holds and we never see. The outbound lanes. JSON callbacks, dictation-system autotext, and HL7 v2 ORU^R01 over MLLP, shaped to whatever interface engine the customer actually runs. Visibility. Per-study tracing, three-component health for each integration, checks for drift between what we think is deployed and what is, and continuous integration for both gateway repositories in your first week. The technical answers in a security review. Precise, honest answers on transport, retention, and incident handling, and a clear account of what the system does and doesn't do. ## Related Videos - [ZEISS & Microsoft - Building the Next Generation Medical Ecosystem in the Cloud](https://www.wearedevelopers.com/videos/424-zeiss-microsoft-building-the-next-generation-medical-ecosystem-in-the-cloud) - [The Software Bug All Stars - and what we can learn from them](https://www.wearedevelopers.com/videos/423-the-software-bug-all-stars-and-what-we-can-learn-from-them) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [From Bedside Beeps to Interoperable Bytes – Getting your medical devices ready to talk the IEEE 11073 language](https://www.wearedevelopers.com/videos/771-from-bedside-beeps-to-interoperable-bytes-getting-your-medical-devices-ready-to-talk-the-ieee-11073-language) - [Logs in observability - Correlation](https://www.wearedevelopers.com/videos/1430-logs-in-observability-correlation) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)