cloud platforms (e.g., AWS, Azure, GCP). Understand data modelling concepts and best practices. Experience with healthcare data standards (e.g., HL7, FHIR, ICD, SNOMED, DICOM) preferred. Excellent problem-solving and communication skills. Ability to communicate complex ideas effectively, both verbally andin writing. Ability to engage all levels of the More ❯
distribution channels. What do the AI outputs look like in my workflow? The outputs from the AI solutions can be displayed in different ways: DICOM objects stored directly in your PACS, utilizing heat-maps, lines/arrows, bounding boxes, regions of interest, circles and/or volumetric segmentations. How do More ❯
you will enable accurate, timely data sharing throughout our clinical ecosystem. You will also support and guide other engineers on best practices for HL7, DICOM, and potentially FHIR-based workflows. Experience in radiology integrations (e.g. RIS/PACS) is particularly beneficial, though not mandatory. You have experience of Your success … similarly regulated sector. You likely have: Extensive Integration Engineering Background A proven history of configuring and optimising Mirth Connect channels, employing JavaScript for HL7, DICOM, and potentially FHIR transformations. Familiarity with XML/JSON for message validation and data handling; experience with radiology systems (e.g. RIS/PACS) is a … Git). Experience with RESTful APIs is desirable, facilitating broader interoperability where needed. High-Performance & Reliable Data Flows Expertise in scaling solutions for large DICOM images or high-volume HL7 traffic, minimising disruption to clinical services. Cross-Functional Collaboration A track record of working effectively with clinical, architectural, and operational More ❯
