Principal Engineer, AI Platform

About the role

Are you a technical leader who thrives at architecting agentic AI systems and shipping them in production? Do you want to help define how autonomous, tool-using AI agents get built into a real, customer-facing product? If so, we invite you to apply to join our AI Platform team, where we're building the future of investment management software. As a Principal Engineer on the AI Platform team, you will guide the architecture and development of Ridgeline's agentic AI platform while also directly contributing to product delivery. You'll help drive the design of the agentic systems, workflows, and capabilities that power our product, including how those agents access external data and use models efficiently, all in close partnership with the engineers who built the platform's foundation. This role offers broad, cross-company impact, working across teams throughout the firm to shape how AI capabilities are built and adopted, in a collaborative, high-agency team culture. Ridgeline is the first front-to-back system of record for investment managers. Founded by visionary entrepreneur Dave Duffield (co-founder of both PeopleSoft and Workday), the company was created to modernize an industry held back by outdated, disconnected technology. Powered by a single, real-time data set and embedded AI, Ridgeline helps firms automate complexity, accelerate collaboration, and deliver tailored client experiences at scale, without added headcount or risk. Ridgeline is headquartered in Lake Tahoe, with offices in New York, Reno, the Bay Area, Dublin Ireland. Ridgeline is recognized by Fast Company as a “Best Workplace for Innovators,” by Frost & Sullivan as a “Technology Innovation Leader,” and by The Software Report as a “Top 100 Software Company.

Responsibilities

• Define and drive technical strategy and architecture for Ridgeline's AI platform and shared AI capabilities across the company.
• Lead technical decisions that enable engineering teams across the firm to use AI platform capabilities in scalable, reliable, secure, and responsible ways.
• Identify and resolve systemic technical obstacles that limit the availability, usability, extensibility, or reliability of the AI platform, including proactively engaging teams building one-off AI solutions to bring them onto the shared platform.
• Establish engineering standards and architectural approaches that help teams make sound decisions as AI technologies and capabilities evolve, including reliability and evaluation standards for AI models in production.
• Partner across Engineering, Product, Data, and other functions to connect AI platform investments to company priorities and customer outcomes, adapting your working style to collaborate with diverse engineering squads.