• Design, develop, and deploy innovative AI planning algorithms using the latest task planning and scheduling techniques. • Collaborate with cross-functional teams to integrate and optimize AI planning solutions into products and processes. • Determine the system design and deployment architecture for AI software pipeline.

• Define and execute the architectural roadmap for our web and mobile platforms, transitioning to a scalable, service-oriented architecture. • Architect the application layer that integrates our foundational services like the EHR that houses our clinical workflows with our novel services, ensuring low latency and high reliability. • Adopt a move fast delivery cadence to break down complex initiatives into shippable, high-value milestones. • Manage and mentor 2-3 cross-functional squads, implementing career frameworks that foster growth and retention. • Own the live site health by establishing SRE (Site Reliability Engineering) practices, incident response protocols, and comprehensive observability standards. • Build a diverse and inclusive engineering culture that attracts top talent and balances autonomy with alignment. • Enforce rigorous CI/CD pipelines and automated testing standards to ensure safe and frequent deployments.

• Lead the development of complex features (e.g., a new intake workflow or provider dashboard) from concept to launch. • Collaborate with the Product & Engineering leadership to define requirements, user flows, and success metrics. You are the Product Manager for your scope. • Design scalable, maintainable full-stack solutions (React, Node.js, Python) that integrate seamlessly with our EHR and AI services. • Manage the timeline and delivery of your projects, coordinating across design, clinical ops, and other engineering squads. • Watch clinicians use your software, look at the data, and iterate rapidly. You are responsible for the success of the feature, not just the code. • Elevate the team's product sense, teaching junior engineers how to think about user value and business impact.

• 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!