Veear
Responsibilities:
Architect and lead the design of enterprise-scale data and AI platforms, ensuring alignment with business strategy, scalability goals, and regulatory requirements (e.g., HIPAA, GDPR).
Define and evolve the company's AI-first architecture strategy, covering data pipelines, model lifecycle management, MLOps practices, and responsible AI governance.
Design and implement secure, scalable MLOps frameworks and model governance to ensure reproducibility, traceability, and auditability of AI solutions across the enterprise.
Integrate data and systems across platforms, including Azure, SAP (S/4HANA, BW, Datasphere), Microsoft Dynamics, Salesforce, and Aras PLM into the Microsoft Fabric ecosystem.
Collaborate with product, engineering, clinical, and regulatory teams to embed AI into medical devices, diagnostics, digital health platforms, and internal productivity solutions.
Enable self-service analytics, reusable AI components, and standardized data assets to empower business and clinical teams across the organization.
Translate business, clinical, and operational requirements into secure, scalable data and AI solutions, bridging technical and non-technical stakeholders.
Define and champion enterprise-wide best practices in data quality, lineage, metadata management, security, and compliance.
Provide technical leadership and mentorship to cross-functional teams (data engineers, ML engineers, data scientists) and guide solution architecture across initiatives.
Continuously research and evaluate emerging trends in Generative AI, cloud-native data/ML platforms, responsible AI, and MedTech regulatory innovation.
Essential Functions of the Role:
Act as the enterprise-wide technical authority for data and AI architecture, ensuring consistency, scalability, and alignment with business goals. Translate complex business and clinical challenges into scalable, AI-enabled solutions that drive measurable impact. Design and guide implementation of core AI infrastructure components, including data lakes, feature stores, model registries, and real-time inference pipelines. Ensure AI systems are ethical, explainable, and auditable, in compliance with internal policies and external regulatory frameworks. Foster a culture of innovation through rapid prototyping, experimentation, and agile iteration-leading initiatives such as internal data and AI hackathons to accelerate learning, surface new use cases, and drive cross-functional engagement. Serve as a visible thought leader by representing the company in external forums related to AI, healthcare technology, and regulatory innovation. 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 Must be fluent in English; written and spoken
Act as the enterprise-wide technical authority for data and AI architecture, ensuring consistency, scalability, and alignment with business goals. Translate complex business and clinical challenges into scalable, AI-enabled solutions that drive measurable impact. Design and guide implementation of core AI infrastructure components, including data lakes, feature stores, model registries, and real-time inference pipelines. Ensure AI systems are ethical, explainable, and auditable, in compliance with internal policies and external regulatory frameworks. Foster a culture of innovation through rapid prototyping, experimentation, and agile iteration-leading initiatives such as internal data and AI hackathons to accelerate learning, surface new use cases, and drive cross-functional engagement. Serve as a visible thought leader by representing the company in external forums related to AI, healthcare technology, and regulatory innovation. 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 Must be fluent in English; written and spoken