Applied AI/ML Solutions Developer

As an Applied AI Scientist, your primary responsibility is to design, build, and deploy advanced AI solutions that encompass traditional machine learning, generative AI, and agentic workflows to address complex business and regulatory needs. You will collaborate closely with stakeholders from various departments such as Product, Operations, Underwriting, Claims, Legal, Compliance, and Risk, ensuring alignment with the broader Technology organization. Your focus will be on developing RAG pipelines, assistants, forecasting and classification models, regulatory intelligence and filing automation, and domain-specific knowledge bases. It is crucial to emphasize responsible AI, governance, and compliance in all your solutions. Key Responsibilities: - Design & Deliver AI Solutions: Build statistical, ML, and generative/agentic AI solutions covering RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems. - Regulatory Intelligence & Filing Automation: Implement GenAI capabilities for automating regulatory filing support and ensuring direct API integrations with regulatory bodies. - Knowledge Base Engineering for Strategic Domains: Develop and maintain domain-specific knowledge bases to support generative applications in underwriting, pricing, and service areas. - Domain & Compliance Integration: Embed domain taxonomies, regulatory constraints, access controls, and security into solution design while adhering to responsible AI practices. - Unstructured Data & Retrieval Design: Prepare multi-format content with normalization, metadata management, and PII detection/redaction, and design retrieval strategies for efficient data access. - Prompt & Agent Design: Author system prompts, structured outputs, and define safe tool-use policies for reliable agent behavior. - Evaluation & Monitoring: Define metrics, build test sets, support A/B testing, and monitor drift to ensure the effectiveness of AI solutions. - Synthetic Data Generation & Augmentation: Develop synthetic data pipelines to accelerate convergence while maintaining privacy and distributional fidelity. - Customer Experience Optimization: Utilize GenAI to enhance self-service, virtual assistants, and inspection automation for improved customer experience. - Architectural Collaboration & MLOps Integration: Partner with enterprise architects and platform teams to ensure scalable and secure AI deployments. Qualifications Required: - 5-7 years of professional experience with a Bachelors degree, or 3-5 years of experience with a Masters or Ph.D. in Machine Learning, Data Science, or related field. - Proficiency in Python, including the use of pandas, NumPy, scikit-learn, and strong SQL for data exploration and feature development. - Experience across the end-to-end modeling lifecycle, including deep learning architectures and model evaluation. - Strong communication skills for translating technical outcomes into measurable business impact. - Experience working with cloud-based AI platforms and deploying models in production systems. - Exposure to NLP and Generative AI capabilities, and knowledge of enterprise AI governance expectations. The above responsibilities and qualifications will position you to excel as an Applied AI Scientist in a dynamic and innovative environment. As an Applied AI Scientist, your primary responsibility is to design, build, and deploy advanced AI solutions that encompass traditional machine learning, generative AI, and agentic workflows to address complex business and regulatory needs. You will collaborate closely with stakeholders from various departments such as Product, Operations, Underwriting, Claims, Legal, Compliance, and Risk, ensuring alignment with the broader Technology organization. Your focus will be on developing RAG pipelines, assistants, forecasting and classification models, regulatory intelligence and filing automation, and domain-specific knowledge bases. It is crucial to emphasize responsible AI, governance, and compliance in all your solutions. Key Responsibilities: - Design & Deliver AI Solutions: Build statistical, ML, and generative/agentic AI solutions covering RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems. - Regulatory Intelligence & Filing Automation: Implement GenAI capabilities for automating regulatory filing support and ensuring direct API integrations with regulatory bodies. - Knowledge Base Engineering for Strategic Domains: Develop and maintain domain-specific knowledge bases to support generative applications in underwriting, pricing, and service areas. - Domain & Compliance Integration: Embed domain taxonomies, regulatory constraints, access controls, and security into solution design while adhering to responsible AI practices. - Unstructured Data & Retrieval Design: Prepare multi-format content with normalization, metadata management, and PII detection/redaction, and design retrieval strategies for efficient data access. - Pro