• Advises on ClaimsXten clinical content and rules/policies, including documentation requirements, client customizations, and configuration options. • Leads client-facing discussions, serving as subject matter expert explaining ClaimsXten edit logic, content sourcing, rationale and expected outcomes to organizations that utilize Lyric’s edits for pre-payment editing. • Maintains working understanding of Lyric’s product behavior to explain and troubleshoot edit outcomes (e.g., triggers, rule logic, content overlaps, configuration dependencies). • Reviews data analytics to assess impacts to content changes and new CPT/HCPCS/ICD10CM code set updates. • Supports client escalations related to Pre-Payment edits, including analysis of scenarios and articulation of recommended resolution paths. • Serves as Clinical liaison across Clinical Operations, Client Success, and other cross functional Lyric teams to increase value of ClaimsXten edits to clients.

• Oversee the Clinical Solutions publishing program across global and local portfolios, including books and digital products • Develop and drive content strategy, including reviewing proposals, financials, and portfolio roadmap analyses • Ensure a strong pipeline of strategic programs and effective content leverage across platforms • Monitor market dynamics and integrate best practices to maintain competitive leadership • Align content initiatives with broader priorities, including portfolio optimization and platform integration • Partner with business units to support electronic solutions, translations, and content localization • Optimize financial performance by managing costs, reviewing proposals, and supporting healthy gross margins • Track and analyze performance metrics, including financials, reporting, and operational insights

Role Description The AI Technical Lead is responsible for leading the design, architecture, development, and deployment of enterprise-scale Generative AI and Agentic AI solutions that drive business transformation and innovation. This role combines deep technical expertise in Large Language Models (LLMs), Agentic AI systems, cloud-native AI platforms, and AI engineering best practices with strong customer engagement and technical leadership capabilities. - Lead the end-to-end design and delivery of Generative AI and Agentic AI solutions. - Define reference architectures, implementation patterns, and engineering standards for enterprise AI applications. - Evaluate emerging AI technologies and frameworks and recommend adoption strategies. - Ensure AI solutions align with enterprise architecture, security, governance, and scalability requirements. - Architect and implement Agentic AI systems, including: - Multi-agent workflows - Agent orchestration patterns - Memory architectures - Tool integration frameworks - Retrieval-Augmented Generation (RAG) solutions - Design AI agents capable of interacting with enterprise systems, APIs, business processes, and knowledge repositories. - Establish reusable frameworks for autonomous and semi-autonomous agent execution. - Lead development of AI-powered applications utilizing LLMs, foundation models, and enterprise knowledge sources. - Guide engineering teams in implementing production-grade AI architectures and services. - Define and implement AI evaluation frameworks covering: - Response quality - Groundedness - Hallucination detection - Agent performance - Safety and reliability - Establish AI monitoring, observability, and operational performance standards. - Support responsible AI, governance, compliance, and model risk management practices. - Develop metrics and measurement frameworks to assess business and technical outcomes. - Design, deploy, and optimize AI solutions across Azure, AWS, and Google Cloud environments. - Collaborate with platform teams to establish scalable and secure AI infrastructure. - Drive adoption of cloud-native architectures, LLMOps, MLOps, and automation practices. - Champion adoption of AI-assisted development platforms including: - Claude Code - GitHub Copilot - Cursor - Equivalent AI engineering tools - Work directly with customers to understand business challenges and identify AI-driven opportunities. - Translate business requirements into scalable technical architectures and implementation plans. - Lead technical workshops, architecture reviews, proofs of concept, and solution demonstrations. - Serve as a trusted advisor on AI strategy, implementation, and operationalization. Qualifications - Advanced Python development - Large Language Models (LLMs) - Generative AI application development - Retrieval-Augmented Generation (RAG) - Agentic AI architectures - Multi-agent systems - Model Context Protocol (MCP) - Tool-calling and agent orchestration frameworks - AI evaluation and observability platforms - Cloud platforms (Azure, AWS, Google Cloud) - API design and integration - Enterprise application architecture Requirements - Experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar agent orchestration frameworks. - Experience implementing AI governance, responsible AI, and model risk management frameworks. - Familiarity with MLOps, LLMOps, CI/CD pipelines, and cloud-native deployment models. - Experience leading enterprise-scale AI transformation initiatives. - Banking & Financial Services industry experience, including exposure to customer servicing, operations, risk, underwriting, fraud, claims, or compliance use cases. Education and/or Experience - Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field. - 10+ years of experience in Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or related technical disciplines. - Demonstrated experience leading complex AI solution delivery programs from concept through production deployment. - Proven experience working directly with business stakeholders and customers in consulting or solution delivery environments.

• Support supplier onboarding from initial engagement through activation • Manage the onboarding process for assigned suppliers, ensuring a smooth and efficient launch experience • Follow established onboarding processes, playbooks, and documentation to maintain consistency and quality • Track and achieve onboarding KPIs, including time to launch, activation rates, and content quality standards • Coordinate onboarding activities across multiple internal teams • Build and maintain supplier relationships • Serve as the primary point of contact for suppliers throughout onboarding and early-stage development • Provide guidance, training, and best-practice recommendations to help suppliers succeed on the platform • Respond to supplier questions and proactively address onboarding challenges • Escalate complex issues when necessary while maintaining a positive supplier experience • Manage content onboarding and quality assurance • Review supplier content to ensure it meets platform standards and quality expectations • Optimize product descriptions, inclusions, meeting points, imagery, and other content for conversion • Conduct quality assurance reviews before products go live • Partner with suppliers to resolve content gaps and improve overall product quality • Support technical onboarding and integrations • Work closely with Supply Operations and technical teams to support supplier integrations • Assist with onboarding-related technical tasks and connectivity validation • Troubleshoot day-to-day onboarding issues and identify whether they are operational or technical in nature • Collaborate with internal teams to ensure issues are resolved efficiently • Contribute to process improvement and reporting • Identify onboarding bottlenecks and opportunities to improve efficiency • Recommend updates to onboarding workflows, documentation, and playbooks • Track supplier onboarding performance and share insights with the broader team • Support initiatives that improve supplier activation, content quality, and long-term supplier success • Leverage AI to optimize and automate repetitive tasks and processes