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 GCP. • 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