AI Innovation and Deployment Specialist

As an Applied AI Scientist, you are responsible for designing, building, and deploying advanced AI solutions that address complex business and regulatory needs. You will work closely with stakeholders across various departments such as Product, Operations, Underwriting, Claims, Legal, Compliance, and Risk, while collaborating with different teams within the Technology organization to deliver scalable, secure, and production-ready solutions. Key Responsibilities: - Design & Deliver AI Solutions: You will build statistical, machine learning, and generative/agentic AI solutions, including RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems using a suitable toolkit. - Regulatory Intelligence & Filing Automation: Your role involves designing and deploying GenAI capabilities to automate regulatory filing support and ensure compliance with evolving standards. - Knowledge Base Engineering for Strategic Domains: You will engineer and maintain domain-specific knowledge bases to power generative applications across underwriting, pricing, and service areas. - Domain & Compliance Integration: Developing a deep understanding of the business structures, processes, and data sources of the company, embedding domain taxonomies, regulatory constraints, access controls, and security into the solution design. - Unstructured Data & Retrieval Design: Prepare multi-format content with normalization and design retrieval strategies while leveraging rerankers where appropriate. - Prompt & Agent Design: Author system prompts, few-shot patterns, and structured outputs to define safe tool-use policies and structured calling for reliable agent behavior. - Evaluation & Monitoring: Define metrics, build test sets, support A/B testing, and monitor drift to provide analysis supporting decisions. - Synthetic Data Generation & Augmentation: Develop and validate synthetic data pipelines to accelerate convergence while preserving privacy and fidelity. - Customer Experience Optimization: Apply AI to enhance self-service, virtual assistants, and inspection automation to drive personalization and operational efficiency. - Architectural Collaboration & MLOps Integration: Partner with enterprise architects and platform teams to ensure scalable and secure deployment of AI solutions. Qualification Required: - Deep applied ML and GenAI expertise - Strong Python and cloud skills - Rigorous evaluation and MLOps practices (Note: Additional details about the company were not provided in the job description) As an Applied AI Scientist, you are responsible for designing, building, and deploying advanced AI solutions that address complex business and regulatory needs. You will work closely with stakeholders across various departments such as Product, Operations, Underwriting, Claims, Legal, Compliance, and Risk, while collaborating with different teams within the Technology organization to deliver scalable, secure, and production-ready solutions. Key Responsibilities: - Design & Deliver AI Solutions: You will build statistical, machine learning, and generative/agentic AI solutions, including RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems using a suitable toolkit. - Regulatory Intelligence & Filing Automation: Your role involves designing and deploying GenAI capabilities to automate regulatory filing support and ensure compliance with evolving standards. - Knowledge Base Engineering for Strategic Domains: You will engineer and maintain domain-specific knowledge bases to power generative applications across underwriting, pricing, and service areas. - Domain & Compliance Integration: Developing a deep understanding of the business structures, processes, and data sources of the company, embedding domain taxonomies, regulatory constraints, access controls, and security into the solution design. - Unstructured Data & Retrieval Design: Prepare multi-format content with normalization and design retrieval strategies while leveraging rerankers where appropriate. - Prompt & Agent Design: Author system prompts, few-shot patterns, and structured outputs to define safe tool-use policies and structured calling for reliable agent behavior. - Evaluation & Monitoring: Define metrics, build test sets, support A/B testing, and monitor drift to provide analysis supporting decisions. - Synthetic Data Generation & Augmentation: Develop and validate synthetic data pipelines to accelerate convergence while preserving privacy and fidelity. - Customer Experience Optimization: Apply AI to enhance self-service, virtual assistants, and inspection automation to drive personalization and operational efficiency. - Architectural Collaboration & MLOps Integration: Partner with enterprise architects and platform teams to ensure scalable and secure deployment of AI solutions. Qualification Required: - Deep applied ML and GenAI expertise - Strong Python and cloud skills - Rigorous evaluation and MLOps practices (Not