• AI System Development : Design and implement AI systems that leverage large language models for legal document processing and analysis • Prompt Engineering : Develop and optimize prompts for effective model interactions in legal contexts • Model Fine-tuning : Fine-tune and adapt models for specialized legal tasks and workflows • Evaluation Framework : Create robust testing and evaluation frameworks to ensure AI system reliability and accuracy • Integration : Work closely with backend teams to integrate AI capabilities into our existing infrastructure • Research & Innovation : Stay current with AI advancements and identify opportunities for applying new technologies

• Act as a strategic automation advisor to enterprise clients • Conduct automation discovery workshops and capability assessments • Design end-to-end automation architectures • Lead and actively contribute to the development of agentic automation solutions • Build, configure, and deploy AI-powered agents • Develop and integrate Gemini-powered generative AI capabilities into automation pipelines • Communicate complex automation and AI concepts clearly to stakeholders • Manage technical delivery risk and maintain high client satisfaction

Role Description This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion). The AI Product and Data Architecture Manager is responsible for understanding the opportunities brought by emerging technologies and Artificial Intelligence to the end-to-end insurance Claims process. This role is critical in the evolution of AF’s current product line functionality via identification of AI-powered capabilities. As part of an AI-powered solution designing and implementation organization, this role will partner with AF’s stakeholders to identify opportunities to embed intelligent decisioning in the claims management ecosystem, modularizing current architecture via re-engineering, modularization, and shifting functional architectures to cloud. The AI Product and Data Architecture Manager articulates the strategy for the definition and implementation of configurable and interoperable AI solutions and plug-and-play scenarios that provide our members and partners with options to realize the value of AF’s offerings, while minimizing technical debt and optimizing the value delivered. This role collaborates with AI Innovation, Advanced Analytics, Operations, Marketing, Membership Experience, and IT functions to execute strategic plans that bring innovative solutions and excellent value to our members’ organization. The AI Product and Data Architecture Manager is a critical player in AF’s journey to data-driven and AI-powered innovation, identifying and implementing strategies to enable the design, development, and implementation of complete and interoperable functional and technical capabilities. Qualifications - More than 10 years of experience in operationalization of AI models and integration with business processes and applications. - More than 8 years of experience in product architecture, product management, and systems interoperability. - Domain and industry knowledge, with deep understanding of how Insurance companies and Claims Management divisions work. - Experience with technology-driven transformations and end-to-end AI solutions lifecycle. - Deep understanding of machine learning/AI principles and concepts, and how AI solutions integrate with existing business processes or applications. - Proficiency in the definition and implementation of prioritization frameworks. - Expertise in cloud application solutions, API design and management, and marketplace business and deployment models. - Functional knowledge of composable products with a high level of configurability. - Working knowledge and proficiency in the development of implementation and adoption plans for composable offerings, including documentation of configuration requirements. - Proficiency in the operations of a not-for-profit organization. - Knowledge of advanced data analytics platforms and visualization tools to create reports and dashboards in a self-service approach. Requirements - Working knowledge of cloud services (MS Azure, AWS) and data platforms (Snowflake, Databricks). - Experience with AI tools, such as MS Azure AI Foundry and ML Studio, Snowflake Cortex AI, Dataiku. - Proficiency in programming languages such as Python, R, or SQL. - Experience designing integrations using tools such as MS Azure API Management, Boomi, MuleSoft, and Apigee. Benefits - Acts as a role model within and outside AF. - Performs duties as workload necessitates. - Maintains a positive and respectful attitude. - Communicates regularly with the departmental leader about department issues. - Demonstrates flexible and efficient time management and ability to prioritize workload. - Consistently reports to work on time, prepared to perform duties of the position. - Meets Department productivity standards. - Alignment with AF Leadership Brand. - Meets department documentation/process guidelines. Company Description AF is a remote working environment.

• 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.