Security Operations Engineer
DeepHealth
Security Operations Engineer
DeepHealth
AI-driven medical imaging and informatics platform
The Security Operations Engineer builds and operates security controls, tooling, and automation across DeepHealth's cloud and corporate environments. This role is responsible for configuring, integrating, and maintaining the enterprise security tooling stack, developing detection content, and automating security operations tasks so that controls are repeatable, version-controlled, and auditable.
Working within DeepHealth's established security frameworks and under the direction of the Director, Security Operations, this position operates and tunes security controls, detection content, and guardrails. Independent validation of those controls sits with the security operations watch function — a deliberate separation that keeps the control environment defensible under audit. Remediation follows system ownership across Cloud Operations, Platform, and Application Development; this role supplies technical guidance and implements fixes directly where the control is security-owned.
This position operates within a regulated healthcare environment across a global operating footprint, with obligations under HIPAA, ISO/IEC 27001, and SOC 2. The role reports to the Director, Security Operations, with future reporting to the Manager, Security Operations as that position is established.
AI-driven medical imaging and informatics platform
DeepHealth provides AI-powered medical imaging and health informatics solutions that help radiologists analyze X-rays, CTs, and MRIs by automating parts of image interpretation, highlighting areas of concern, and delivering quantitative insights. It works by integrating AI into radiology workflows via cloud-based software, enabling automated image analysis, scoring, and reporting within enterprise imaging systems. The company differentiates itself through its integration with RadNet and strategic acquisitions of iCAD and CIMAR UK, expanding breast imaging capabilities, interoperability, and the scale of its deployments. Its goal is to accelerate the adoption of AI in real-world healthcare, improve diagnostic accuracy and consistency, and streamline imaging workflows across hospitals and health systems.
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