Principal Data and AI Architect

Requirements:

• 10 15 years of progressive experience in data architecture, machine learning engineering, and AI platform development.
• Proven track record delivering production-grade AI/ML solutions in cloud and/or edge environments.
• Experience in regulated industries such as MedTech, Pharma, or Healthcare; strong preference for familiarity with compliance and validation workflows.
• Hands-on expertise with modern data and AI technologies, such as Databricks, Snowflake, MLflow, Kubernetes, TensorFlow, and PyTorch.
• Bachelor's (required) or Master's degree in Computer Science, Information Systems, Engineering, or a related technical field.
• Advanced degree and/or certifications in one or more of the following areas is preferred: Artificial Intelligence, Machine Learning, Data Engineering, Cloud Architecture, or Enterprise Solution Architecture.
• Relevant certifications may include:
• Microsoft Certified: Azure Solutions Architect Expert
• Azure AI Engineer Associate
• Azure Data Engineer Associate
• AWS Certified Solutions Architect Professional
• Databricks Certified Data Engineer Professional

Specialized Skills/Technical Knowledge:

• Deep expertise in AI/ML model development, including MLOps practices, model lifecycle management, and responsible AI principles (explainability, bias mitigation, auditability).
• Strong understanding of cloud-native data architecture, including Microsoft Azure (preferred), AWS, or Google Cloud Platform.
• Hands-on experience with the Microsoft Fabric ecosystem, including Data Factory, OneLake, Power BI, and integration with Azure AI and DevOps services.
• Experience designing and integrating data pipelines across SAP data platforms (S/4HANA, BW, Datasphere), Microsoft Dynamics, and Product Lifecycle Management (PLM) systems (e.g., Aras).
• Proficiency with Generative AI frameworks, Copilot integrations, and prompt engineering for productivity and business automation use cases.
• In-depth knowledge of regulatory frameworks such as ISO 13485, FDA 21 CFR Part 11, and data privacy laws (e.g., HIPAA, GDPR), especially in handling PHI/PII securely.
• Proven ability to embed privacy, security, and compliance controls into AI and data solutions, including encryption, access governance, and audit trails.
• Exceptional collaboration and communication skills, with the ability to influence technical and non-technical stakeholders at all organizational levels.
• Passion for healthcare innovation and a strong commitment to improving patient outcomes through ethical and intelligent use of data and AI.
• 20% travel; domestic and international