We're seeking a Database Engineer to architect and optimize our large-scale RAG (Retrieval-Augmented Generation) platform that serves our users across all of the Hardware Tech group. This role combines deep database expertise with modern AI/ML infrastructure, enabling design teams to seamlessly onboard and query enterprise-scale datasets. You'll be responsible for database architecture and optimization while also contributing to full-stack GenAI application development.
As a Database Engineer on our team, you will architect and optimize our SQL and vector database infrastructure supporting enterprise-scale design data. You'll lead technical decisions on database architecture, scaling patterns, and technology selection for our RAG platform while designing comprehensive strategies to ensure optimal performance. Working closely with the development team, you'll build and refine data ingestion pipelines that enable design teams across all disciplines to seamlessly onboard their data. You'll collaborate with DevOps/SRE teams to ensure quality of service, proper resource allocation, and system scalability while improving RAG retrieval performance through hybrid search strategies, index tuning, and embedding optimization. In addition to your primary database focus, you'll contribute to full-stack development using Python and JavaScript, monitor database health and performance metrics for our multi-tenant system, and develop and maintain database operations procedures, monitoring, and disaster recovery strategies while driving continuous improvement of retrieval quality, search latency, and overall system reliability. You'll also provide mentorship to other engineers on database best practices and scalable design patterns.
Proficiency in Python or Javascript.
Production experience deploying and managing vector databases (Milvus, Qdrant, or Weaviate) at scale
Experience with PostgreSQL or MySQL in production environments
Understanding of RAG pipelines, including embedding strategies, chunking, and retrieval optimization
Minimum requirement of BS + 10 years of relevant industry experienceUnderstanding of Vector database indexing strategies and tradeoffs
Strong SQL proficiency with deep understanding of query planning, indexing strategies, and optimization techniques
Postgres advanced features (extensions, replication, sharding)
Experience managing large-scale databases serving high-concurrency workloads
Experience with embedding models and LLM integration patterns
Demonstrated experience building or optimizing RAG systems in production environments
Collaborative mindset with ability to mentor engineers and work closely with DevOps/SRE teams
Monitoring and observability tools (Prometheus, Grafana)
Kubernetes experience, particularly with stateful applications and database deployments
Proven ability to make architectural decisions for scalable database systems
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