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Metro location and broad multi-discipline skillset increase applicant density moderately despite seniority.
Requires deep ML/AI platform and regulated-healthcare experience, limiting easy cross-industry transferability.
Explicit 15+ years, mandatory ML/GenAI leadership, cloud and regulated-environment experience create strict filters.
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Define and lead multi-year technical vision and architecture principles for enterprise-scale AI applications and platforms ensuring security, scalability, and cost-efficiency.
Provide technical leadership and oversight across AI initiatives including model selection frameworks, solution design reviews, and delivery of measurable business value.
Collaborate cross-functionally to embed security, privacy, ethical AI practices, and establish standards for performance monitoring, governance, and continuous improvement.
15+ years experience in software/AI engineering with technical leadership mainly through influence rather than formal management.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (advanced degree preferred).
Expertise in distributed, cloud-native systems, microservices, and data architectures with proven proficiency in ML and Generative AI solution patterns at scale.
Proficiency with modern tech stacks (Python, JavaScript/TypeScript, front-end frameworks, backend APIs), cloud platforms (AWS, Azure, GCP), and experience embedding security, privacy-by-design, and responsible AI practices.
Experienced leader capable of influencing senior engineers and cross-organizational stakeholders to adopt AI technologies responsibly and effectively without direct line authority.
Strong strategic thinker skilled in defining architecture standards and technology roadmaps for enterprise AI platforms focused on business outcomes and risk management.
Expert at navigating cloud-native AI/ML ecosystems and integrating emerging technologies like multimodal models, vector DBs, and advanced governance tooling in regulated environments.