Staff AI Data Platform Engineer San Ramon, CA Reno, NV

Ridgeline is the first front-to-back system of record for investment managers. Founded by visionary entrepreneur Dave Duffield (co-founder of both People Soft and Workday), the company was created to modernize an industry held back by outdated, disconnected technology.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, and the Bay Area, and 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 building the connective tissue that will power the next generation of its data and AI strategy — and we're looking for a senior engineer to help build it. This isn't a governance role, and it isn't a support function. It's hands‑on engineering with real, visible business impact: the systems you build will directly enable how Ridgeline stores, connects, and activates its data across the business.

You'll join a team at an inflection point. What started as a connectivity-focused function is being asked to own something much bigger — the architecture behind secure integrations, Model Context Protocols (MCPs), API gateways, and the AI platform (RAG pipelines, vector databases, model provider gateways) that will define how Ridgeline uses AI responsibly and effectively. You won't be maintaining someone else's roadmap.

You'll be helping write it.

• Build the platform that powers AI-driven decisions. Design and ship secure MCPs and API integrations that connect Ridgeline's data sources to the business capabilities that depend on them.
• Shape a strategy, not just a system. Help move the team from "we connect things" to "we own how data is stored, persisted, and connected" — a shift that puts you at the center of Ridgeline's broader data strategy.
• Push the AI platform forward. Contribute to model provider gateways, RAG pipelines, and vector database implementations that keep Ridgeline ahead of the curve in its industry.
• Raise the bar. This is an uplevel hire — you'll be expected to bring senior judgment, mentor peers, and help set a higher technical standard for the team as it scales.

• First 30 days: You're ramped on the tech stack, the team's ways of working, and the systems you'll be building on.
• First 90 days: You're operating like an owner — driving your own projects to completion with minimal hand holding, and showing you can adapt quickly to a fast-moving environment.
• Ongoing: You're a visible contributor to the team's AI-forward capabilities, a strong collaborator across a matrixed org, and someone peers point to as making the team better.

• 7+ years of senior-level engineering experience, with a track record of owning problems end-to-end — from identifying the issue to proposing and driving the solution.
• Experience designing and scaling distributed systems — you understand the tradeoffs of consistency, availability, and performance at scale, not just how to stand up a service.
• Hands‑on experience building secure API integrations, with the depth to extend that into newer protocols like MCPs — you can speak fluently about the security considerations involved (modern auth patterns like OAuth 2.0 and SSO included). MCPs are barely two years old, so we're not expecting years of MCP-specific tenure — just proven judgment in secure integration architecture that transfers.
• Real AI fluency, personally and professionally — you use AI-assisted coding tools like Claude Code or Cursor as part of your own workflow and can talk in depth about your own AI journey, not just name-drop the tools.
• Comfort with modern data platforms beyond standard relational databases — think Snowflake, vector databases (e.g., Pinecone, Weaviate, pgvector), and similar technologies.
• Familiarity
  with data lineage, access control, and data quality practices — especially relevant given the regulated nature of the investment…