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Leads strategic planning, budgeting, forecasting, and financial analysis for the risk exchange platform. Partners with finance, accounting, actuarial, and distribution teams; analyzes operational and insurance results; prepares management reporting; supports monthly close; and presents insights to senior leadership. Drives Pigment planning-platform adoption and improves FP&A processes through AI, automation, documentation, and digital tools.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

Manage onboarding, policy administration, and bordereaux workflows; ensure data integrity and SLA adherence; produce reports using Snowflake, Power BI, and Excel; collaborate cross-functionally to document SOPs and implement process improvements.

Design, build, and operate scalable web applications, APIs, services, and AI-enabled workflows. Use AI tools across the development lifecycle, ensure reliability, performance, and observability, contribute to technical decisions, iterate from user feedback, and own production quality and delivery as part of cross-functional squads.

As a Full Stack Engineer, you will develop frontend and backend features, write robust code, and collaborate with team members to meet project requirements.

Lead discovery, roadmap, and delivery of data platform capabilities for the Accelerant Risk Exchange. Define strategy, prioritize ingestion/consumption features, collaborate with stakeholders and engineering, champion AI-native experimentation, and communicate outcomes to internal and external partners.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead and own ML platform end-to-end: data/feature pipelines, training and inference infrastructure, model registry, deployment, monitoring, and agentic AI stack. Design integrations across teams and third parties, ensure reproducibility, reliability, cost control, governance and audit trails, and grow a small engineering function supporting data scientists.

Design, implement, and operate secure, scalable multi-cloud infrastructure (Azure/AWS) with IaC (Terraform), managed Kubernetes, containers, and AI platform support. Lead complex projects, define operational processes, ensure security/compliance, mentor teammates, and maintain observability for production systems.

The Business Engagement Lead - Finance Operations ensures alignment between Finance and Technology, overseeing business requirements, product roadmaps, and stakeholder engagement while monitoring outcomes and driving technology investments to match business goals.