Staff AI Data Platform Engineer
$138,500–$173,000 year
On-siteSan Ramon, California, United States
Job Summary
Design and ship secure MCPs and API integrations that connect Ridgeline's data sources to business capabilities. Contribute to model provider gateways, RAG pipelines, and vector database implementations that enable AI-driven decisions. Drive the team's shift from connectivity-focused functions to owning how data is stored, persisted, and connected across the enterprise. Mentor peers and set a higher technical standard as you help write the architecture for secure integrations and the AI platform. Work in a matrixed environment with security and platform teams to build creative, modern solutions that define how Ridgeline uses AI responsibly.
Required Qualifications
- 7+ years of senior-level engineering experience
- Track record of owning problems end-to-end — from identifying the issue to proposing and driving the solution
- Experience designing and scaling distributed systems
- Understanding of tradeoffs of consistency, availability, and performance at scale
- Hands-on experience building secure API integrations
- Ability to speak fluently about security considerations involved in secure integration architecture
- Modern auth patterns knowledge (OAuth 2.0 and SSO)
- Real AI fluency, personally and professionally
- Use of AI-assisted coding tools like Claude Code or Cursor as part of own workflow
- Ability to talk in depth about own AI journey
- Comfort with modern data platforms beyond standard relational databases
- Familiarity with Snowflake
- Familiarity with vector databases (e.g., Pinecone, Weaviate, pgvector)
- Familiarity with data lineage, access control, and data quality practices
- Cloud engineering fundamentals (AWS)
- Infrastructure-as-code (Terraform or similar)
- Ability to write production-quality code in Python or a comparable language
Desired Qualifications
- 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
- Personal AI projects — running local models, building agent-to-agent workflows, or similar experimentation
- Experience mentoring or upleveling junior and mid-level engineers
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