• Architect and drive the strategic vision for AI/ML platforms that power document ingestion, understanding, and extraction across multiple product teams and use cases. • Establish organization-wide frameworks and standards for dataset quality, ML validation methodologies, and model verification processes that ensure healthcare-grade safety and compliance. • Define core ML infrastructure for LLM operations, including model serving platforms, fine-tuning pipelines, and generative AI systems that enable teams to build reliable document understanding applications at scale. • Lead the technical architecture for production capabilities. • Build and optimize the core RAG infrastructure, vector database architecture, and LLM integration patterns that enable efficient retrieval and structured output generation for downstream systems. • Drive cross-functional technical strategy, partnering with product, clinical, and engineering leadership to align multi-quarter ML roadmaps with business requirements and regulatory compliance. • Mentor junior/mid AI/ML engineers across distributed teams, establish best practices for model training, evaluation, and monitoring, and define ML engineering standards that scale across the organization. • Stay abreast of the latest advancements in generative AI and lead technical evaluation and adoption of cutting-edge techniques that provide measurable business value.

• Own the full development lifecycle — from idea to architecture, implementation, testing, and delivery of real-world AI products • Design and build robust AI systems, including Iris.ai ’s proprietary Retrieval-Augmented Generation (RAG) pipelines and LLM Evaluation Frameworks used by leading R&D teams • Drive innovation by testing new approaches and bringing new AI research into production • Collaborate across teams — work closely with researchers, product managers, and fellow engineers to shape and ship powerful features • Build systems for performance and reliability, ensuring they are scalable, testable, and maintainable • Contribute to a global mission to transform how the world interacts with scientific knowledge and applied AI • Thrive in a remote-first, agile environment, contributing meaningfully within a distributed, deep-tech team • Keep learning, always — we’ll support you with space, mentorship, and resources to grow as an AI engineer

• Design and develop AI agents using frameworks such as LangGraph and MCP for Workday use cases • Build AI-driven experience layers using CopilotKit, Vercel AI SDK, or similar tools • Enable scalable AI deployments on AWS or Azure • Develop AI assets and integration frameworks supporting AI SDLC • Design and implement Workday integrations using Workday APIs and integration tools (Studio, EIB, REST/SOAP) • Work with Workday data structures (HCM, Finance) for context-aware AI solutions • Integrate AI agents with incident/ticketing systems (e.g., ServiceNow) • Collaborate with Workday functional and technical teams to deliver solutions • Support automation of service requests, diagnostics, and operational workflows

• Design and develop AI agents using frameworks such as LangGraph and Model Context Protocol (MCP) for Oracle use cases • Build AI agent experience layers using tools like CopilotKit, Vercel AI SDK, or similar • Enable AI workloads on AWS, Azure, or Oracle Cloud Infrastructure (OCI) • Develop AI coding assets and MCP-based integrations to support AI SDLC · Design and implement integrations with Oracle systems (Oracle Fusion, EBS, OCI services) using REST/SOAP APIs • Work with Oracle data models to enable context-aware AI workflows • Integrate AI solutions with incident and ticketing systems (e.g., ServiceNow) • Collaborate with Oracle functional, technical, and infrastructure teams for delivery • Support diagnostics, automation, and optimization of Oracle managed services operations