Experience designing, developing, and deploying conversational AI solutions, including chatbots, virtual assistants, intelligent agents, and Retrieval-Augmented Generation (RAG) pipelines.
Experience working with unstructured data, including document parsing and OCR fundamentals, robust text normalization, metadata and lineage awareness, and PII detection or redaction considerations for insurance documents.
Experience working with cloud-based AI and analytics platforms such as Google Vertex AI, AWS SageMaker or Bedrock, or Azure AI Services, supporting experimentation, deployment, and conversational AI use cases.
Experience using Git and Unix-based environments, building reproducible notebooks or pipelines, and applying basic container and cloud concepts to support reliable analytics and AI workflows.
Experience communicating complex technical designs, trade-offs, evaluation results, and risks to both highly technical and non-technical business audiences, translating insights into clear business outcomes and strategy.
Foundational knowledge of insurance products, processes (including underwriting, claims, and pricing), and regulatory environments, with demonstrated ability to apply analytics and AI solutions within a regulated industry context.
Experience with advanced NLP and Generative AI capabilities, including embeddings, hybrid and dense retrieval strategies, advanced chunking, prompt engineering, structured outputs, and integrating knowledge graphs into RAG solutions for improved grounding.
Experience or exposure to advanced GenAI applications in insurance, such as compliance-aware prompt engineering, document generation, objection response automation, synthetic data generation for low-frequency events, and customer sentiment modeling from surveys, call transcripts, or inspection notes.
Experience working within enterprise AI governance frameworks, aligning conversational AI and analytics solutions with compliance, privacy, documentation, and ethical standards.
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