• Lead the reference architecture for AI and agentic systems within key product domains, defining standards and reusable patterns for reasoning loops and orchestration, tool/function calling, memory and state management, data integration and retrieval, safety, governance and interoperability. • Design and deliver production ready AI systems, ensuring reliability, scalability, observability, performance, and cost efficiency across the full lifecycle. • Establish platform level AI and data components, including Model broker and multi-model routing strategies, orchestration and workflow services, evaluation and experimentation frameworks, prompt, configuration, and versioning systems and telemetry, monitoring, and cost governance tooling. • Lead the design of end-to-end AI data pipelines, including document ingestion, OCR, NER, and preprocessing, chunking, embeddings, vector stores, and hybrid search and offline and online evaluation for quality, latency, safety, and cost. • Partner with data and analytics teams to align AI solutions with measurable product and business outcomes. • Partner with Product Management to identify, shape, and validate AI driven opportunities that improve clinical, operational, and business outcomes. • Help product teams move from problem framing to AI enabled solutions, balancing feasibility, value, risk, and regulatory constraints. • Contribute to innovation efforts by evaluating emerging AI tools, models, and frameworks. • Prototyping new approaches in collaboration with Advanced Technology. • Translating successful experiments into scalable platform capabilities. • Ensure all AI solutions are production grade, with strong practices for observability, logging, alerting, and incident response, performance testing and cost optimization, safe rollout, experimentation, and rollback strategies. • Embed responsible AI and security by design principles, including RBAC, audit logging, and privacy controls, alignment with healthcare regulations and standards (e.g., HIPAA, NIST AI RMF, ONC aligned guidance) and close collaboration with Security, Privacy, Regulatory, and Platform teams. • Mentor and coach senior and intermediate engineers, raising overall capability in AI and data architecture, production AI engineering practices, evaluation, experimentation, and systems thinking. • Drive adoption of shared AI and data best practices, enabling teams to reuse platform components, contribute improvements back to shared frameworks, consistently evolve domain specific AI capabilities.

• Lead the reference architecture for AI and agentic systems within key product domains, defining standards and reusable patterns for reasoning loops and orchestration, tool/function calling, memory and state management, data integration and retrieval, safety, governance and interoperability. • Design and deliver production ready AI systems, ensuring reliability, scalability, observability, performance, and cost efficiency across the full lifecycle. • Establish platform level AI and data components, including Model broker and multi-model routing strategies, orchestration and workflow services, evaluation and experimentation frameworks, prompt, configuration, and versioning systems and telemetry, monitoring, and cost governance tooling. • Lead the design of end-to-end AI data pipelines, including document ingestion, OCR, NER, and preprocessing, chunking, embeddings, vector stores, and hybrid search and offline and online evaluation for quality, latency, safety, and cost. • Partner with data and analytics teams to align AI solutions with measurable product and business outcomes. • Partner with Product Management to identify, shape, and validate AI driven opportunities that improve clinical, operational, and business outcomes. • Help product teams move from problem framing to AI enabled solutions, balancing feasibility, value, risk, and regulatory constraints. • Contribute to innovation efforts by evaluating emerging AI tools, models, and frameworks • Prototyping new approaches in collaboration with Advanced Technology. • Translating successful experiments into scalable platform capabilities. • Ensure all AI solutions are production grade, with strong practices for observability, logging, alerting, and incident response, performance testing and cost optimization, safe rollout, experimentation, and rollback strategies. • Embed responsible AI and security by design principles, including RBAC, audit logging, and privacy controls, alignment with healthcare regulations and standards (e.g., HIPAA, NIST AI RMF, ONC aligned guidance) and close collaboration with Security, Privacy, Regulatory, and Platform teams. • Mentor and coach senior and intermediate engineers, raising overall capability in AI and data architecture, production AI engineering practices, evaluation, experimentation, and systems thinking. • Drive adoption of shared AI and data best practices, enabling teams to reuse platform components, contribute improvements back to shared frameworks, consistently evolve domain specific AI capabilities.

• Design, build, and maintain full-stack web applications and backend services • Own features end-to-end from discovery and scoping through implementation, deployment, and post-launch measurement • Partner directly with product and business stakeholders to define hypotheses, evaluate trade-offs, and align on success metrics • Design, implement, and measure A/B tests and experimentation initiatives using tools such as PostHog or similar platforms • Build and maintain full-stack TypeScript applications using frameworks such as Astro and Vue • Contribute to backend services in PHP (Laravel) where applicable • Write and optimize SQL queries and work directly with relational databases both within and outside of ORMs • Work with cloud and edge infrastructure, including Cloudflare Workers, Cloudflare D1, and AWS EKS • Contribute to CI/CD pipelines using GitHub Actions and participate in structured Git workflows, including branching, pull requests, and code reviews • Debug and troubleshoot issues across the full stack with minimal oversight • Improve observability, reliability, and performance using monitoring tools such as Datadog • Participate in architectural discussions and contribute to long-term technical strategy • Conduct thoughtful code reviews and mentor early-stage engineers • Leverage AI-assisted development tools to improve velocity and code quality

• Design, build, and maintain full-stack applications that support our internal platform • Own delivery of features from technical design through deployment and post-release support • Collaborate closely with Product Managers to clarify requirements and design pragmatic technical solutions • Build and maintain backend services using PHP (Laravel/Lumen) or Python • Contribute to frontend functionality using JavaScript where needed • Develop APIs and integrations with internal and third-party systems • Write clean, maintainable, and testable code • Work within AWS-based infrastructure and Git-based deployment workflows • Participate in structured code reviews and uphold engineering quality standards • Troubleshoot and resolve production issues when they arise • Contribute to architectural discussions and continuous improvement initiatives • Leverage AI-assisted development tools to improve efficiency and quality