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About the Role

Drive AI engineering workstreams across the Audit+ program, ensuring compliance of UW and Claims processing, automation of controls, and cost reduction. This role will identify and build production-grade foundational capabilities, platform them for rapid operationalization, and scale Audit+ AI capabilities, focusing on end-to-end automation across Claims, UW, Finance, and Technology.

Responsibilities

• Develop the next generation of AI driven Audit+ platforms and AI assets, including Agentic framework
• Build scalable pipelines for data ingestion, feature engineering, model training, evaluation, and monitoring
• Develop and integrate generative AI applications, including LLM-based workflows, agents, and retrieval-augmented generation (RAG) solutions
• Ensure solutions meet security, privacy, compliance, and responsible AI standards
• Optimize model performance, reliability, latency, and cost across the AI lifecycle
• Platform capabilities for extending AI enablement to human-lead operations in areas like QA, Training | Operations for faster and efficient production and introduce efficiencies in distribution workflows
• Enable sales analytics | marketing with a foundational layer of AI with Consumer LLM as needed
• Drive implementation and change management in collaboration with regional D&A leads, business and technology partners
• Work with the Consumer+ Platform Engineering team to develop reusable Foundational AI Assets | Applications to accelerate local deployments
• Support Business Development by evangelizing our AI success stories to stakeholders and sponsors as needed
• Enable the regional and local teams to leverage Global Consumer+ platforms and be self-sufficient
• Work closely with regional Data & Analytics teams to identify opportunities and assist in implementation
• Collaborate with Regional IT, GDO, Global Analytics, Ops for data | infra | integration related to implementation
• Collaborate with teams to enforce responsible AI, model risk management, and AI governance

Skills

• Generative AI
• LLM-based workflows
• Retrieval-augmented generation (RAG)
• Responsible AI standards
• Model risk management
• AI governance