Job description

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. What will you do? 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. Architect solutions for integrating external data via retrieval-augmented generation, including connections to third-party data stores, and build systems for token-efficient AI usage such as model routing and code execution. Propose and evaluate new AI features to advance the platform's capabilities, and resolve technical debates to align teams around the right approach. Desired Skills and Experience 15+ years of software engineering experience, with deep expertise in systems architecture. Significant experience building and shipping an AI-powered product in production, including agentic loop design, structured responses, tool calling, Model Context Protocol (MCP), and retrieval-augmented generation (RAG). Proven ability to design scalable, efficient system architectures and evaluate the right technologies for AI system components. Experience establishing software development best practices and reliability/evaluation standards for AI models in production. Full-stack experience across front-end and back-end AI product development; proficiency in Kotlin, TypeScript, and React. Experience working with both Anthropic and/or OpenAI models and APIs. Track record of driving technical or architectural decisions and aligning stakeholders across teams, especially amid competing viewpoints. Demonstrated success integrating third-party data sources and delivering measurable improvements in AI model performance and token efficiency in production. Bonus Experience with multi-agent systems, AI agent orchestration, or advanced model evaluation techniques. Background in financial services or investment management. Contributions to open-source AI/ML projects or publications in the space. About Ridgeline 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. Ridgeline is proud to be a community-minded, discrimination-free equal opportunity workplace. Ridgeline processes the information you submit in connection with your application in accordance with the Ridgeline Candidate Privacy Policy . Please review the Ridgeline Candidate Privacy Policy in full to understand our privacy practices and contact us with any questions. This posting is for an existing vacancy. Compensation and Benefits The typical starting salary range for new hires in this role is listed below. In select locations (including, the San Francisco Bay Area, CA, and the New York City Metro Area), an alternate range may apply as specified below. The typical starting salary range for this role is: $253,000-$316,000. The typical starting salary range for this role in the select locations listed above is: $278,000-$347,500. Final compensation amounts are determined by multiple factors, including candidate experience and expertise, and may vary from the amount listed above. As an employee at Ridgeline, you’ll have many opportunities for advancement in your career and can make a true impact on the product. In addition to the base salary, Ridgeline employees can participate in our Company Stock Plan subject to the applicable Stock Option Agreement. We also offer rich benefits that reflect the kind of organization we want to be: one in which our employees feel valued and are inspired to bring their best selves to work. These include unlimited vacation, educational and wellness reimbursements, and $0 cost employee insurance plans. Please check out our Careers page for a more comprehensive overview of our perks and benefits. #LI-Remote

Requirements

Must-haves

What will you do?
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.
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.
Propose and evaluate new AI features to advance the platform's capabilities, and resolve technical debates to align teams around the right approach.
Desired Skills and Experience
15+ years of software engineering experience, with deep expertise in systems architecture.
Significant experience building and shipping an AI-powered product in production, including agentic loop design, structured responses, tool calling, Model Context Protocol (MCP), and retrieval-augmented generation (RAG).
Proven ability to design scalable, efficient system architectures and evaluate the right technologies for AI system components.
Experience establishing software development best practices and reliability/evaluation standards for AI models in production.
Full-stack experience across front-end and back-end AI product development; proficiency in Kotlin, TypeScript, and React.
Experience working with both Anthropic and/or OpenAI models and APIs.
Track record of driving technical or architectural decisions and aligning stakeholders across teams, especially amid competing viewpoints.
Demonstrated success integrating third-party data sources and delivering measurable improvements in AI model performance and token efficiency in production.

Nice-to-haves

Experience with multi-agent systems, AI agent orchestration, or advanced model evaluation techniques.
Background in financial services or investment management.
Contributions to open-source AI/ML projects or publications in the space.

Application prep

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