Job Description Summary

Software Systems Architect responsible for leading architecture of interoperable medical device ecosystems across embedded devices, connectivity platforms, and healthcare systems. Drives design of scalable, secure device-to-cloud and system-to-system integrations using standards such as HL7 and IHE. Provides deep technical leadership across embedded (RTOS), connectivity, and cloud architectures, ensuring cohesive and reliable end-to-end solutions. Partners with cross-functional teams to ensure compliance with regulatory, cybersecurity, and safety requirements while supporting verification and validation strategies. Influences product direction, industry standards, and innovation to advance interoperability and improve clinical outcomes.

Job Description

We are the makers of possible 

 

BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities. 

 

We believe that the human element, across our global teams, is what allows us to continually evolve. Join us and discover an environment in which you’ll be supported to learn, grow and become your best self. Become a maker of possible with us. 

Role Summary

The Medication Management Solutions (MMS) organization at BD is seeking a Principal-level Software Systems Architect to define and drive the end-to-end system architecture for interoperable, AI-enabled medical device ecosystems. This role operates at a system-of-systems level, spanning embedded bedside devices, connectivity platforms, cloud services, analytics pipelines, and integrations with healthcare information systems within the Internet of Medical Devices (IoMD).

The architect will own architectural strategy, system decomposition, interface contracts, and cross-domain design decisions that ensure scalable, secure, and clinically safe interoperability. The role requires deep experience in embedded systems, connectivity, cloud-native architectures, and increasing use of data and AI capabilities to improve clinical workflows, system observability, and patient safety, while operating within regulated medical device constraints.

Principal Objective

Define, govern, and evolve system architecture across multiple product platforms to deliver robust, scalable, compliant, and future-ready interoperability solutions that align with business objectives, clinical workflows, regulatory expectations, and emerging AI-driven capabilities.

Key Responsibilities

System Architecture and Interoperability

• Define and lead end-to-end system architecture across embedded devices, connectivity layers, cloud services, analytics platforms, and healthcare integrations.

• Decompose complex systems into domains, services, and works with clear architectural ownership, responsibilities, and lifecycle boundaries.

• Define interface contracts, data models, and message flows using industry standards such as HL7, IHE, FHIR, JSON, and XML.

• Ensure architectural consistency and reuse across platforms while supporting product-specific constraints and regulatory contexts.

• Drive architectural tradeoff decisions balancing safety, performance, scalability, maintainability, and regulatory risk.

Embedded, Edge, and Cloud Architecture

• Provide architectural leadership across RTOS-based embedded systems running on ARM platforms.

• Define cohesive device, edge, and cloud interaction models, including buffering, resilience, fault handling, and offline behavior.

• Architect cloud-based solutions using Azure and AWS, including event-driven, service-oriented, and data pipeline architectures.

• Guide technology selection and evolution while ensuring backward compatibility and long-term maintainability.

AI, Analytics, and Data Architecture

• Define system architectures that enable AI and advanced analytics across interoperability data, device telemetry, alarms, and clinical workflow signals.

• Guide integration of machine learning and rule-based analytics for use cases such as clinical safety monitoring, alert analysis, workflow optimization, anomaly detection, and system observability.

• Ensure AI-enabled capabilities adhere to explainability, traceability, validation, and risk management expectations appropriate for regulated medical devices.

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