Director of Applied Engineering

The Hartford is seeking a Director of Applied Engineering within Applied Analytics to lead a high-impact team of Analytics Engineers driving the next generation of AI-powered intelligence across the enterprise. This role sits at the intersection of Artificial Intelligence, Business Intelligence, and deep insurance domain expertise — owning the vision, strategy, and delivery of conversational AI agents, agentic analytics workflows. 

As Director, you will build and grow a team of Analytics Engineers who design and deliver sophisticated AI solutions that enable business users to explore complex insurance data, derive actionable insights in plain English, and accelerate data-driven decision-making. You are a leader who understands the technical depth required to build these systems and who can translate that depth into a compelling team roadmap, a high-performing culture, and measurable business outcomes. 

Responsibilities 

• Build, lead, and develop a team of Analytics Engineers, fostering a culture of innovation, technical excellence, and continuous learning focused on agentic analytics and conversational AI. 

• Set clear goals, provide ongoing coaching and feedback, and create development pathways that grow the team's capabilities in AI/ML and domain expertise. 

• Recruit and retain top-tier analytics engineering talent with experience in generative AI, LLMs, RAG pipelines, and BI design. 

• Champion a collaborative, psychologically safe team environment that encourages experimentation and responsible AI development. 

• Define and own the multi-year roadmap for the Applied Analytics function, aligning agentic analytics, conversational AI, and BI initiatives with enterprise data strategy and business priorities. 

• Lead disciplined innovation by balancing delivery excellence with forward-looking investment in emerging AI/analytics technologies and methodologies. 

• Establish the team's technical standards, architectural patterns, and governance frameworks for AI solution development and MLOps practices. 

• Drive the adoption of agentic AI workflows — including multi-agent orchestration, tool-use patterns, and autonomous analytics — across the Applied Analytics team. 

• Own the strategic direction for conversational AI capabilities that allow business users to explore insurance data and derive insights through natural language interfaces. 

• Guide the team in designing and delivering RAG pipelines, intelligent chat/assistant systems, classification, forecasting, and recommendation engines - leveraging a fit-for-purpose toolkit from traditional ML to sophisticated agentic workflows. 

• Set the architectural vision for agent design, including prompt engineering standards, safe tool-use policies, function/structured calling patterns, and guardrails for reliable and ethical agent behavior within the insurance context. 

• Champion responsible AI practices including fairness, bias mitigation, transparency, observability, and compliance-by-design across all conversational and agentic solutions. 

• Lead the strategy and governance for AI-driven BI across insurance lines of business, ensuring consistent, accurate, and business-friendly definitions of facts, dimensions, and metrics. 

• Partner with data engineering, platform, and architecture teams to ensure BI solutions are scalable, maintainable, and directly consumable by AI agents and BI tools. 

• Drive the team's use of dimensional modeling and BI best practices to create a unified view of complex insurance data that accelerates both analytical and AI use cases. 

• Lead the delivery of GenAI capabilities supporting regulatory filing automation, including DOI objection response generation and ingestion of legacy filings into searchable knowledge bases. 

• Ensure the team embeds domain taxonomies, regulatory constraints, access controls, and security directly into solution design. Partner closely with Legal and Compliance to meet evolving standards. 

• Oversee the engineering and maintenance of domain-specific knowledge bases (e.g., regulatory intelligence, competitive insights, customer sentiment) to power generative applications across underwriting, pricing, and service. 

• Lead the team through the full AI solution lifecycle: problem framing, data preparation, model development, evaluation, CI/CD, orchestration, observability, safety, and rollback. 

• Establish and enforce GitHub best practices for version control, documentation, and code collaboration across the analytics engineering lifecycle. 

• Drive standardization of experiment tracking, model registries, evaluation gates, and CI/CD patterns across cloud platforms. 

• Oversee the team's evaluation and monitoring practices — ensuring comprehensive metrics coverage across RAG/chat, classification, forecasting, and operational KPIs — and champion A/B testing and drift detection as standard practice.