Staff AI Data Platform Engineer

Ridgeline•Reno, NV

5h•$138,500 - $173,000•Hybrid

About The Position

Ridgeline is building the connective tissue that will power the next generation of its data and AI strategy. This role is 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.

Requirements

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 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.

Responsibilities

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.
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.
Contribute to model provider gateways, RAG pipelines, and vector database implementations that keep Ridgeline ahead of the curve in its industry.
Bring senior judgment, mentor peers, and help set a higher technical standard for the team as it scales.