• Be the AI Agent expert. Serve as the go-to internal and external expert on Cresta's AI Agent capabilities, engaging early in the sales process to establish technical credibility and accelerate opportunity velocity. • Work hand-in-hand with Core SEs. Partner closely with Core SEs as the AI Agent specialist, owning the AI Agent technical narrative. • Lead AI Agent workshops. Design and facilitate technical discovery workshops with prospective customers to explore AI Agent use cases, define implementation scope, and align on a path to value, translating business problems into agent-powered solutions. • Own use case and API discovery. Lead deep-dive technical discovery sessions to surface, propose, and refine AI Agent use cases. Evaluate integration requirements and API feasibility to give the deal a solid technical foundation. • Support high-stakes AI Agent demos. You step in to own the most complex and high-stakes AI Agent demonstrations in partnership with the Core SE team. • Engage early as a specialist resource. Enter opportunities early, helping to qualify and shape AI Agent deals before proof-of-value, bringing depth and structure to the technical conversation from the start. • Build the business case. Partner with Sales Directors and Core SEs to translate AI Agent solutions into financial ROI models, contributing to ROI workshops and framing the business case in terms that resonate with economic buyers. • Partner with FDX. Collaborate closely with Forward Deployed PMs and Engineers in both directions, ensuring smooth handoff from pre-sale to deployment and pulling FDX learnings on agent performance, edge cases, and customer outcomes back into pre-sale positioning and discovery. • Close the product feedback loop. Synthesize patterns from AI Agent opportunities and provide structured feedback to Product and Engineering, accelerating Cresta's roadmap to stay ahead of enterprise demand. • Elevate the SE team's AI Agent fluency. Share expertise across the broader SE organization through enablement, playbooks, and informal coaching, raising the team's collective capability.

• Provide remote and onsite consulting and implementation services to members around product utilization and best practices. • Analyze internal data to prioritize opportunities effectively and provide a plan. • Perform research and data analysis on Member’s product use to better deliver more value to underutilized products or best practices. • Develop and deliver software training to internal and external stakeholders as needed. • Coordinate and present at related workshops and industry related events as needed. • Build tools to help support the needs assessment of our Membership and how they utilize NISC’s solutions.  • Influence and gain Member interest around implementing new product functionality and/or best practices. • May be asked to record walk through product trainings and recordings with the internal Learning Center. • General expectation is primary focus is within one to multiple products within one to multiple industries. • Maintain an in-depth level understanding of product integrations. • Execute special projects with a moderate to high degree of difficulty with independence as needed or requested.  • May be asked to help participate in NISC’s Member Information Conference, Advisory Meetings and other related events. • May be asked to mentor others. • Proactively engage internal departments and leadership on industry trends related to product road maps and direction. • May provide strategic input to internal and external senior level leaders around product utilization. • Serve as onsite champion and change manager for Member product initiatives. • Low to Moderate level amounts of travel required.

• Lead the architecture, design, and implementation of enterprise AI solutions utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). • Design scalable, secure, and maintainable AI architectures supporting enterprise healthcare applications. • Define solution architecture, data flows, system interfaces, AI orchestration, and integration patterns. • Evaluate AI technologies, services, and frameworks to support current and future project needs. • Ensure architecture supports approved future phases without expanding the authorized MVP scope. • Design AI solutions leveraging Amazon Bedrock and AWS cloud services. • Architect secure AI pipelines supporting approved document access and processing, retrieval, vector search, prompt orchestration, and AI-assisted summarization. • Define strategies for model selection, prompt management, retrieval optimization, and AI performance tuning. • Define monitoring and evaluation strategies to detect model, prompt, retrieval, and data drift and address degradation in accuracy, safety, or clinical relevance. • Optimize AI architectures for scalability, operational cost, reliability, and response time. • Collaborate with DevSecOps teams to support deployment automation and operational readiness. • Design integration between AI services and existing enterprise healthcare applications. • Define secure interfaces utilizing REST APIs and modern integration patterns. • Ensure solutions align with healthcare interoperability standards including FHIR, HL7, and CCD. • Collaborate with application development teams to integrate AI capabilities into clinician workflows. • Promote consistent architecture patterns and engineering best practices across JLV development teams. • Design AI solutions that comply with Federal cybersecurity, privacy, and Responsible AI requirements. • Incorporate Human-in-the-Loop (HITL), source traceability, approved data boundaries, retention and purge controls, explainability, auditability, and governance principles into solution architecture. • Support Authority to Operate (ATO), AI governance, Security Impact Analysis (SIA), and technology approval activities. • Ensure secure handling of Protected Health Information (PHI) and Personally Identifiable Information (PII). • Collaborate with cybersecurity teams to implement secure AI architectures and operational controls. • Serve as the technical leader for AI architecture across the JLV contract. • Mentor software engineers and provide architectural guidance throughout the software development lifecycle. • Participate in architecture reviews, design sessions, sprint planning, backlog refinement, and technical estimation. • Produce architecture documentation, system design artifacts, interface specifications, and implementation guidance. • Present technical approaches and architectural recommendations to Government leadership and stakeholders.

• Serve as the client-side senior authority on SAP data migration; face off to the System Integrator's data lead as a technical peer. • Review and challenge the SI's data migration approach and push back where the approach carries unnecessary risk, cost, or downstream operational burden. • Advise the program on migration architecture decisions specific to greenfield S/4HANA from non-SAP sources (Oracle EBS, JD Edwards, PeopleSoft, custom systems), including source-to-target data model translation and legacy-concept-to-SAP-concept mapping. • Advise on tooling selection when the SI's approach is still open and evaluate trade-offs on fit, cost, timeline, and regulated-industry auditability. • Provide hands-on coordination and delivery. • Own day-to-day execution coordination of the client-side data workstream across all domains. • Keep the migration task plan current, flag slippage early, and drive resolution. • Serve as the daily point of contact with the SI's data migration team; track integrator deliverables and escalate delays. • Run regular working sessions with client data owners and the integrator's team; chase open actions, decisions, and deliverables between meetings. • Set and monitor cleansing standards across data domains against agreed quality thresholds; validate that the SI's quality criteria are fit for S/4HANA go-live. • Coordinate with business data owners to keep cleansing on schedule; unblock issues with the right people rather than deferring upward. • Surface data quality risks the SI may not flag (missing legacy concepts, silent data loss in transformation, dependencies across domains) before they become critical. • Organize and track mock load cycles across all domains. • Review reconciliation results independently of the SI. • Ensure business teams complete validation tasks within cycle windows and that findings are actioned. • Ensure data migration activities comply with the organization's data classification policy and applicable regulatory requirements throughout extraction, staging, transformation, and load. • Maintain the data migration cutover plan; coordinate extraction and load scheduling with the SI and technical teams. • Prepare data readiness inputs for go/no-go decisions and provide informed, independent risk assessment to program leadership. • Support hyper care on data-related issues post go-live. • Support migration status reporting and RAID logging for program leadership.