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