Job Description
OVERVIEW
Drive AI engineering workstreams across the Audit+ program, ensuring compliance of UW and Claims processing, automation of controls, as well as driving cost reduction.
ROLE
This role will work closely with regional teams to identify and build production grade foundational capabilities, platform those capabilities to enable rapid operationalization and scale out of Audit+ AI capabilities. The primary focus is on end-to-end automation of controls across Claims, UW, Finance and Technology.
FOCUS AREAS & 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
Operating Network:
• 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
Qualifications
Candidate Profile:
Technical Skills
• Strong hands-on coding ability in Python plus at least one additional language (TypeScript, Go, or Java); disciplined about clean code, design docs, and code review.
• Deep knowledge of modern LLM tooling and techniques: Hugging Face Transformers, prompt engineering, post-training/fine-tuning pipelines, retrieval-augmented generation (RAG), and agentic AI frameworks.
• Experience with inference optimization and high-throughput serving frameworks.
• Proven experience shipping and operating high-scale services on Docker/Kubernetes with CI/CD pipelines (GitHub Actions, Jenkins, or similar).
• Experience with event-stream/service-integration technologies (e.g., Kafka) and building resilient, observable production systems (SLOs for latency, error rate, availability).
• Experience building end-to-end ML pipelines: data ingestion, feature engineering, model training, evaluation, and monitoring.
• Experience integrating AI/LLM services into user-facing products (APIs, SDKs, real-time UX features).
Governance, Risk & Compliance
• Working knowledge of responsible AI practices, model risk management, and AI governance frameworks.
• Experience Ensuring AI solutions meet security, privacy, and regulatory compliance standards, particularly in audit, underwriting, or claims-adjacent contexts.
Leadership & Collaboration
• Ability to architect and own robust, scalable engineering solutions while remaining hands-on with code.
• Experience partnering cross-functionally with regional Data & Analytics teams, IT, GDO/Ops, front-end, and DevOps stakeholders to drive implementation and change management.
• Ability to represent technical architecture, trade-offs, and AI risk to both engineering leaders and non-technical executives with clarity and confidence.
Attributes
• Outstanding written and verbal communication across technical and executive audiences.
• Bias for action and comfort making high-impact decisions under uncertainty.
• Ability to drive KPI/OKR-based delivery in an iterative, sprint-based environment.
