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沈阳市, 辽宁省, 中国
Onsite
Job title:
As an important member of the R&D team, the AI/LLM Application Architect will lead the architecture, governance, and technical strategy of a controlled AI Harness platform for medical-device R&D. The role is responsible for enabling AI-assisted quality and engineering workflows across requirements, traceability, DHF, risk, validation, and change-impact analysis while ensuring the platform remains compliant, auditable, and verifiable under medical-device quality system expectations.
Your role:
Lead the end-to-end architecture of the AI R&D QMS Harness, including enterprise data access, knowledge/RAG layers, prompt and model governance, AI workflow services, audit logging, and evidence archiving.
Define system boundaries, approved data classes, model whitelisting, prompt governance, and human-in-the-loop controls so that AI outputs can be used in a controlled quality-system environment.
Design and govern the core AI agents for Requirement Quality, Traceability, DHF/Evidence, DFMEA/Risk, Compliance Review, Defect Impact, and Review/Test workflows.
Translate business and QMS requirements into scalable, secure, and validated application architecture across source systems such as ALM, ClearQuest, Windchill, SOP repositories, and DHF repositories.
Establish technical controls for source citation, no-source labeling, RBACversion control, logging, and reproducibility of AI outputs.
Partner with QA, RA, Risk, Systems Engineering, Validation, and IT teams to define AI Use SOPs, risk assessments, OQ/PQ strategy, and validation evidence required for deployment.
Lead technical qualification of AI use cases through URS, OQ, PQ, prompt validation, regression testing, and controlled change management.
Define the target operating model for AI/LLM development, including central harness team structure, SME enablement model, and deployment strategy by geography.
Evaluate model providers, orchestration frameworks, RAG patterns, and deployment options for cloud and local/private inference.
Drive the long-term roadmap for controlled AI in R&D quality systems and create reusable architectural patterns for future expansion.
You're the right fit if:
You have acquired 8+ years of experience in enterprise software, AI/ML platform engineering, LLM applications, or regulated systems architecture, preferably in medical devices, healthcare, life sciences, or other regulated industries.
Your skills include strong experience with AI/LLM application architecture, RAG systems, prompt and model governance, API integration, secure enterprise system design, and audit-ready solution design.
You have hands-on experience with Python and modern AI application stacks, plus experience with enterprise AI platforms such as Azure OpenAI, AWS Bedrock, or equivalent controlled environments.
You understand how to architect human-in-the-loop workflows, evidence capture, and quality gates for AI-assisted systems.
You have working knowledge of regulated development and quality frameworks such as ISO 13485, IEC 62304, ISO 14971, FDA 21 CFR Part 820/11, or comparable standards.
You can explain complex technical and governance concepts clearly to non-specialists, senior stakeholders, and cross-functional quality teams.
You are comfortable leading cross-functional design reviews, aligning architecture decisions with QA/RA expectations, and shaping the validation strategy of AI-enabled workflows.
Bachelor's, Master's, or Ph.D. in Computer Science, Software Engineering, AI/ML, Biomedical Engineering, or a related field.
Experience in medical imaging, QMS tooling, DHF workflows, or regulated engineering environments is strongly preferred.
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If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.