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.
About the Role
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. What You'll Own Team Leadership 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 AI/ML Practice 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 Data Platform 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 Cross-Functional Partnership 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 Tech Stack 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 About you AI-First, Data-Grounded. You know that great AI products are built on great data. You don't treat the platform as a prerequisite, you treat it as a weapon. Technically Credible. You've built models that ran in production. You've debugged a pipeline at 11pm. You can evaluate your team's work, not just manage it. Builder and Operator. You can design from scratch and scale what's already working. You know which mode you're in and you shift between them. Low Ego, High Impact. You care more about the outcome than the credit. You've hired people better than you in their domains and gotten out of their way. Strong Opinions, Weakly Held. You bring a clear point of view to architecture decisions and update it fast when the data says otherwise. Team First. You win through the team. You hire people better than you in their domains and get out of their way.
Requirements
Required 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
Nice to Have
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
Benefits
Full remote flexibility and asynchronous work culture Unlimited PTO and fully paid sick leave Comprehensive health benefits, including medical, dental, and vision coverage, plus HSA and FSA options Additional financial protection and retirement benefits, including a 401(k), company-paid life insurance, and disability coverage A high degree of ownership, autonomy, and the opportunity to help build and shape a growing company The chance to make a meaningful impact while working alongside an ambitious, high-performing team Exposure to the challenges and opportunities of a fast-growing startup environment
Compensation
Base Salary Range $200,000-$250,000 This is a good-faith compensation range based on what Ledgebrook reasonably expects to pay for this position at the time of this posting. Actual compensation may vary based on a variety of relevant factors including experience, qualifications, geographic location and other relevant factors. The salary range is for a full time position but we are open to 4 days/week. Employees in this position are eligible to participate in Ledgebrook’s equity incentive program.