RAG Engineer
Tasks
- Build document ingestion indexing retrieval pipelines
- Design retrieval-augmented generation systems
- Develop APIs and services for AI workflows
- Evaluate retrieval frameworks and search technologies
- Implement and manage vector databases and embedding pipelines
- Implement data security and governance controls
- Implement semantic search across structured and unstructured data
- Integrate retrieval services with large language models
- Maintain operational documentation and procedures
- Monitor retrieval accuracy and system performance
- Optimize retrieval performance and relevance
- Troubleshoot retrieval and indexing issues
- Tune and evaluate retrieval quality
Perks/Benefits
Skills/Tech-stack
AWS | Agile | Azure | Cloud Computing | Cloud platform | Docker | Document ingestion | Embeddings | Google Cloud | Google Cloud Platform | Indexing | Information Retrieval | Kubernetes | Language Models | Large Language Models | Latency optimization | Machine Learning | Microservices | Python | REST APIs | Retrieval-Augmented Generation | Scalability | Semantic Search | Vector Databases | Vectorization
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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