Director / Senior Director of Data & AI Engineering

At Ledgebrook, we are building an Excess & Surplus (E&S) lines insurance company that combines deep underwriting and pricing expertise with a modern tech platform fit for the future of insurance– truly a best of both worlds approach. Our speed and service underpin every decision we make, and our rapidly growing team is a testament to our value proposition resonating with the market. You bring the passion and entrepreneurial spirit, and we’ll provide the opportunity to unleash the very best of your talents and skills. Apply now to advance your career at Ledgebrook.

We're looking for a Director or Senior Director of Data & AI Engineering to lead both our data platform and AI/ML practice. This is a combined, senior role. You'll own a 10-person and growing data engineering team and a 3-person (and growing) AI group, with a mandate to make them work as one integrated capability.

The opportunity: we have rich, structured insurance data composed of underwriting submissions, loss runs, pricing signals, claims history. We're early in unlocking its full value. We want someone who sees that and knows exactly what to build with it. That means classical ML on proprietary datasets, LLM-powered automation across our operations, and the data infrastructure to support all of it.

This is a player-coach role. You'll set technical direction and still get in the weeds when it counts. You'll partner directly with the CTO and work across underwriting, actuarial, finance, and product to make AI a durable competitive advantage.

• Manage, mentor, and grow a 10-person data engineering team and a 3-person AI/ML team; own headcount planning and hiring across both
• Set a unified roadmap where data infrastructure and AI/ML development reinforce each other
• Build a culture of technical rigor, ownership, and delivery

• Lead development of ML models using proprietary insurance data: risk scoring, pricing signals, anomaly detection, loss prediction
• Own LLM integration strategy from prompt engineering and RAG pipelines to fine-tuning and agentic workflows
• Drive AI automation across operations: underwriting intake, document processing, triage, internal tooling
• Partner with the CTO on enterprise AI platform decisions: tooling, deployment infrastructure, model governance
• Build the evaluation, monitoring, and feedback loops that turn experiments into production systems

• Set architectural standards for pipelines, data modeling, and platform infrastructure
• Own reliability, observability, and data quality across Snowflake, dbt, Airflow, and Terraform
• Build semantic layers and data models that serve underwriting, pricing, finance, and executive reporting
• Establish data governance, quality frameworks, and documentation standards that scale

• Collaborate with actuaries, underwriters, engineers, and product leaders to translate business needs into AI and data solutions
• Operate as a senior technical voice in planning, roadmap, and strategy discussions

• Languages: Python, SQL
• Data Stack: Snowflake, dbt, Apache Airflow (AWS MWAA)
• Cloud Infrastructure: AWS, Terraform
• AI/ML: LLM APIs (OpenAI, Anthropic), vector databases, ML frameworks (scikit-learn, PyTorch or equivalent)
• BI: Tableau
• Tools: GitHub, Jira, Confluence, Slack

• 8+ years across data engineering, ML engineering, or AI/data science with meaningful depth in at least two of those
• 3+ years managing technical teams, with experience leading both data and ML/AI practitioners
• Hands-on fluency in Python and SQL; comfort reviewing production ML code and data pipelines
• Experience building and deploying ML models against structured business data (pricing, risk, fraud, or equivalent)
• Production experience with LLMs - RAG architectures, prompt design, agentic frameworks, or fine-tuning
• Strong grounding in modern data stack tooling (Snowflake, dbt, Airflow, Terraform or equivalents)
• History of taking AI/ML work from prototype to reliable production system

• Experience in insurance, fintech, or other data-rich regulated domains
• Familiarity with MLflow, Weights & Biases, or similar model lifecycle tooling
• Experience with OCR, document intelligence, or unstructured data pipelines
• Background bridging data science and data engineering org structures