Job description

Senior Staff AI Infrastructure Engineer San Ramon, CA or Reno, NV 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Duffield (co-founder of both PeopleSoft and Workday), the company was created to modernize an industry held back by outdated, disconnected technology.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, 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.” The Opportunity 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. Your Impact 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. What Success Looks Like 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 handholding, 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. What You'll Need 10+ 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 management industry we operate in. A collaborative, no-ego approach. Technical excellence matters, but so does being a good teammate — we're explicitly not looking for brilliant jerks. Cloud engineering fundamentals (AWS), infrastructure-as-code (Terraform or similar), and the ability to write production-quality code in Python or a comparable language — everyone on this team is an engineer first. Nice to Have Experience and proficient level of understanding using RAG pipelines, LLMs, and vector database implementations in production. Exposure to model provider gateways or local/open-source model routing. Understand how it’s used and where it’s used. Personal AI projects — running local models, building agent-to-agent workflows, or similar experimentation. Experience mentoring or upleveling junior and mid-level engineers. The Team You're Joining It's a close-knit, high-performing team of three dedicated engineers. You'll work in a matrixed environment alongside security, platform, and Leveraged Engineering teams, with the autonomy to build creative, modern solutions rather than being handed a spec to execute. Have an engineering mindset first and someone who understands cloud infrastructure, writes code, and stays current as the landscape shifts. In return, you get real ownership, a seat at the table on strategy that matters, and a team that will have your back while you figure out the best way to solve hard problems. Compensation and Benefits The typical starting salary range for this role is: $138,500 - $173,000. 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 plaﬁs. Please check out our Careers page for a more comprehensive overview of our perks and benefits. Equal Employment Opportunity (EEO) Statement Our company does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factor. 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.

Requirements

Must-haves

Senior Staff AI Infrastructure Engineer
San Ramon, CA or Reno, NV
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.
What Success Looks Like
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 handholding, 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.
What You'll Need
10+ 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.
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 management industry we operate in.
A collaborative, no-ego approach. Technical excellence matters, but so does being a good teammate — we're explicitly not looking for brilliant jerks.
Cloud engineering fundamentals (AWS), infrastructure-as-code (Terraform or similar), and the ability to write production-quality code in Python or a comparable language — everyone on this team is an engineer first.

Nice-to-haves

Experience and proficient level of understanding using RAG pipelines, LLMs, and vector database implementations in production.
Exposure to model provider gateways or local/open-source model routing. Understand how it’s used and where it’s used.
Personal AI projects — running local models, building agent-to-agent workflows, or similar experimentation.
Experience mentoring or upleveling junior and mid-level engineers.
The Team You're Joining

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