• Drive the technical roadmap for AI/ML systems, aligning model development and deployment initiatives with key business outcomes. • Establish and champion scalable AI delivery practices (evaluation, deployment, monitoring, governance) and production-grade architecture patterns and engineering standards. • Identify and address strategic technical debt and performance bottlenecks within the core AI/ML platform and inference services. • Lead the adoption of emerging AI research, foundational models, and deep learning frameworks that future-proof our platform's intelligence capabilities. • Build agentic workflows that can query metrics, run analyses, and cite supporting data • Implement RAG patterns over internal schemas/data • Establish an evaluation and guardrails framework: monitoring for drift/hallucinations, along with PII handling, tenant isolation, policy controls, and audit logging. • Oversee production pipelines for AI services and ML workflows, including deployment, monitoring, and governance. • Lead the development and evolution of scalable tools for prompt/agent versioning, experimentation, evaluation, release management, and monitoring. • Ensure robust data validation, model testing practices, continuous integration/continuous delivery (CI/CD) for ML models, and automated deployment of AI services are in place. • Promote engineering best practices for building explainable, ethical, and bias-aware AI systems through agile methodologies and continuous improvement. • Build, mentor, and grow high-performing AI Engineering, Data Science, and AI Research teams and technical leaders. • Partner with executive leadership to define and execute technical strategies for integrating AI across product lines and departments. • Foster a culture of rigorous scientific experimentation, accountability in model performance, and cross-functional collaboration between research, data, and engineering. • Serve as a senior technical advisor on complex AI/ML architectural decisions, providing guidance on model selection and influencing data product direction. • Whatever else it takes to get the job done!