Principal Engineer - RAG Database & Embeddings Architect
Charlotte, United States
USD 140K-200K (estimate) Senior-level Full Time
Tasks
- Communicate AI and data engineering concepts to stakeholders
- Define embedding strategies across content types
- Define engineering approach for program portfolio solutions
- Define observability for retrieval quality and risk indicators
- Define technology tool stack
- Design and own enterprise RAG data stores architecture
- Design vectorization workflows and re embedding lifecycle
- Establish vector schema standards and namespace strategy
- Evaluate and select embedding models
- Implement semantic keyword metadata filtered and hybrid retrieval
- Lead end-to-end test strategy and integration
- Lead evaluation of vector database platforms and retrieval frameworks
- Lead planning definition and design across multiple teams
- Optimize retrieval quality with similarity reranking and query expansion
- Provide architecture guidance and mentoring across AI platform teams
- Provide technical oversight and design reviews
Perks/Benefits
- N/A
Skills/Tech-stack
API Design | Access Control | Approximate Nearest Neighbor | Database Design | Distributed Systems | Embeddings | Hybrid retrieval | Indexing | Keyword search | Latency optimization | Metadata Modeling | Nearest neighbor | Observability | Performance Tuning | Precisión | Query Expansion | RAG | Recall | Reranking | Retrieval-Augmented Generation | Semantic Search | Similarity Search | Vector Database | Vectorization
Education
Roles
Architect | Engineer | Principal | Principal Engineer | Software Architect
Regions
Countries
States
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