• In collaboration with the office of the CEO and VP Of AI Factory, execute on the multi-year technical roadmap for AI engineering, aligned with the company's strategic goals across care delivery, cost management, and member experience. • Serve as the senior engineering voice for AI at the executive level, partnering with VP Of AI Factory, influencing platform investment, architecture direction, and build-vs-buy decisions across the enterprise. • Partner with the CTO, Chief Data Officer, VP Of AI Factory and clinical leadership to ensure AI engineering capabilities support the full AI lifecycle from research and experimentation to scalable production deployment. • Architect and operate a cloud-native, enterprise-grade AI platform that supports model training, evaluation, versioning, deployment, and monitoring at scale. • Establish and enforce MLOps best practices including CI/CD pipelines for model development, automated testing, model registry management, and drift detection. • Drive infrastructure strategy across compute, orchestration (e.g., Kubernetes, Airflow), and data pipelines to optimize cost, performance, and regulatory compliance. • Lead engineering teams responsible for developing, fine-tuning, and deploying machine learning models and LLM-powered solutions into production healthcare environments. • Oversee integration of AI models with core health plan systems including claims platforms, EHRs, care management tools, and member-facing applications ensuring high availability, low latency, and auditability. • Champion rigorous model evaluation, A/B testing, and continuous improvement frameworks appropriate for high-stakes healthcare use cases. • Recruit, develop, and retain a world-class team of AI engineers, ML engineers, and data scientist with deep healthcare domain exposure. • Build an engineering culture rooted in technical ownership, clinical accountability, psychological safety, and continuous learning. • Define career frameworks, leveling guides, and growth paths for the AI engineering organization. • Establish and enforce AI engineering standards covering responsible AI, bias detection, model explainability, and clinical safety in alignment with HIPAA, CMS regulations, and applicable state requirements. • Ensure all AI systems and infrastructure meet the company's security, data privacy, and compliance standards, protecting sensitive member and clinical data. • Partner with Office of CEO, Legal, Compliance, Security, and Risk teams to identify, assess, and mitigate technical and ethical risks associated with AI deployments, including third-party and vendor AI solutions. • Develop and maintain AI governance policies and audit frameworks to support regulatory reporting and internal oversight. • Collaborate with Office of CEO, clinical, operational, and business stakeholders to translate complex engineering capabilities into clear, actionable AI solutions. • Communicate AI engineering strategy, platform performance, and risk posture to executive leadership and board-level stakeholders with clarity and confidence. • Work closely with vendor and technology partners to evaluate and integrate external AI capabilities into the enterprise platform. • Define and own engineering KPIs including model performance, system reliability (SLOs/SLAs), deployment velocity, infrastructure cost efficiency, and clinical outcome metrics. • Provide regular executive reporting on AI engineering progress, translating technical performance into measurable business and clinical impact. • Lead cost optimization efforts across AI infrastructure without compromising platform capability or compliance posture.

• Design and execute software development workflows that leverage AI agents for code generation, refactoring, debugging, testing, documentation, and delivery. • Help build and improve the internal tools, automation layers, interfaces, and workflows that enable AI coding agents. • Build, test, deploy, and maintain internal tools, integrations, automations, and product features across back-end, front-end, and infrastructure layers. • Manage branches, pull requests, merges, reviews, and repository hygiene with strong discipline and ownership. • Work efficiently in terminal environments using CLI-based workflows, remote development setups, and AI-native developer tools. • Configure and manage SSH keys, access credentials, environment variables, remote systems, and development security best practices. • Troubleshoot issues across applications, APIs, databases, infrastructure, environments, and tooling layers. • Identify repetitive engineering and operational tasks, then design automations and developer tooling to reduce manual work and improve speed. • Work closely with operations, product, and leadership to translate business needs into reliable technical systems. • Stay current with AI coding tools, agentic development patterns, and modern software engineering workflows that can improve team performance.

• Design and execute software development workflows that leverage AI agents for code generation, refactoring, debugging, testing, documentation, and delivery while maintaining strong engineering judgment and code quality. • Help build and improve the internal tools, automation layers, interfaces, and workflows that enable AI coding agents to operate effectively in our environment. • Build, test, deploy, and maintain internal tools, integrations, automations, and product features across back-end, front-end, and infrastructure layers as needed. • Manage branches, pull requests, merges, reviews, and repository hygiene with strong discipline and ownership. • Work efficiently in modern terminal environments using CLI-based workflows, remote development setups, AI-native developer tools, and related systems. • Configure and manage SSH keys, access credentials, environment variables, remote systems, and development security best practices. • Work with MCP servers and related systems to connect AI tools to development context, internal systems, and operational workflows. • Ensure agent-assisted and human-authored code meets high standards of correctness, readability, maintainability, and test coverage. • Troubleshoot issues across applications, APIs, databases, infrastructure, environments, and tooling layers. • Identify repetitive engineering and operational tasks, then design automations and developer tooling to reduce manual work and improve speed. • Work closely with operations, product, and leadership to translate business needs into reliable technical systems. • Stay current with AI coding tools, agentic development patterns, and modern software engineering workflows that can improve team performance.

• You will lead the development and deployment of scalable, efficient AI models, optimizing performance, scalability, and cost. • You will work closely with data scientists, software engineers, and product managers to integrate AI solutions into larger systems and ensure they are robust, functional, and scalable. • You will analyze large volumes of complex data and develop advanced predictive models that effectively solve client-specific problems. • You will be responsible for the continuous improvement of existing systems, implementing strategies to optimize performance, efficiency, and reliability. • You will integrate AI solutions with cloud platforms (AWS, Google Cloud, Azure), leveraging their capabilities to improve performance and reduce costs. • You will maintain an innovative mindset, researching the latest trends in AI, machine learning, and emerging technologies to apply to current and future projects. • You will document complex technical processes and communicate results and innovations to both technical and non-technical stakeholders.