Applied AI/ML Solutions Developer

As an Applied AI Scientist, you will be responsible for designing, building, and deploying advanced AI solutions that encompass traditional machine learning, generative AI, and agentic workflows to meet complex business and regulatory needs. You will collaborate closely with stakeholders from various departments such as Product, Operations, Underwriting, Claims, Legal, Compliance, and Risk, aligning with the broader Technology organization to deliver scalable, secure, and production-ready solutions. Your primary focus will be developing RAG pipelines, assistants, forecasting and classification models, regulatory intelligence and filing automation, and domain-specific knowledge bases. Emphasis will be placed on responsible AI, governance, and compliance throughout the AI lifecycle.

Key Responsibilities:

• Design & Deliver AI Solutions: Utilize a variety of statistical, machine learning, and generative/agentic AI techniques to build solutions like RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems. Employ a toolkit ranging from traditional predictive modeling to agentic workflows.
• Regulatory Intelligence & Filing Automation: Implement GenAI capabilities to automate regulatory filing support, including DOI objection response generation and integration of legacy filings into searchable knowledge bases. Collaborate with Legal and Compliance to ensure outputs meet standards and enable API integrations with regulatory bodies.
• Knowledge Base Engineering for Strategic Domains: Create and maintain domain-specific knowledge bases (e.g., regulatory intelligence, competitive insights, customer sentiment) to support generative applications in underwriting, pricing, and service.
• Domain & Compliance Integration: Gain a deep understanding of the business structures, processes, and data sources at The Hartford. Incorporate domain taxonomies, regulatory constraints, access controls, and security into solution design. Ensure adherence to responsible AI practices with compliance-by-design principles.
• Unstructured Data & Retrieval Design: Prepare and manage multi-format content with normalization, metadata/lineage management, and PII detection/redaction. Develop retrieval strategies and tuning for cost, latency, and domain suitability, leveraging rerankers when necessary.
• Prompt & Agent Design: Create robust system prompts, few-shot patterns, and structured outputs like JSON schemas. Establish safe tool-use policies and structured calling for reliable agent behavior.
• Evaluation & Monitoring: Define metrics for use cases such as classification, information retrieval, RAG/chat, forecasting, and customer/ops KPIs. Construct test sets, support A/B testing, and monitor drift. Provide analysis supporting decisions with economic, qualitative, and statistical insights.
• Synthetic Data Generation & Augmentation: Build and validate synthetic data pipelines to address sparsity and accelerate convergence, particularly for low-frequency perils and emerging segments while maintaining privacy and fidelity.
• Customer Experience Optimization: Utilize GenAI to enhance self-service, virtual assistants, and inspection automation to drive personalization, speed, and operational efficiency.
• Architectural Collaboration & MLOps Integration: Collaborate with enterprise architects and platform teams to ensure scalable, secure deployment of AI solutions. As an Applied AI Scientist, you will be responsible for designing, building, and deploying advanced AI solutions that encompass traditional machine learning, generative AI, and agentic workflows to meet complex business and regulatory needs. You will collaborate closely with stakeholders from various departments such as Product, Operations, Underwriting, Claims, Legal, Compliance, and Risk, aligning with the broader Technology organization to deliver scalable, secure, and production-ready solutions. Your primary focus will be developing RAG pipelines, assistants, forecasting and classification models, regulatory intelligence and filing automation, and domain-specific knowledge bases. Emphasis will be placed on responsible AI, governance, and compliance throughout the AI lifecycle.

Key Responsibilities:

• Design & Deliver AI Solutions: Utilize a variety of statistical, machine learning, and generative/agentic AI techniques to build solutions like RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems. Employ a toolkit ranging from traditional predictive modeling to agentic workflows.
• Regulatory Intelligence & Filing Automation: Implement GenAI capabilities to automate regulatory filing support, including DOI objection response generation and integration of legacy filings into searchable knowledge bases. Collaborate with Legal and Compliance to ensure outputs meet standards and enable API integrations with regulatory bodies.
• Knowledge Base Engineering for Strategic Domains: Create and maintain domain-specific knowled