Data Scientist
Tasks
- Build evaluation frameworks for retrieval and groundedness
- Build vector search and hybrid retrieval pipelines
- Capture and utilize user feedback for continuous improvement
- Clean and prepare data for chunk based retrieval workflows
- Design conversational UX with citations and confidence indicators
- Design production grade AI systems for large language models
- Develop RAG architectures
- Develop lightweight frontend applications with Flask
- Implement prompting strategies
- Implement text-to-SQL workflows
- Manage prompt versioning model deployments and rollback strategies
- Monitor latency cost reliability and response quality
- Process unstructured documents from PDF DOCX HTML OCR sources
Perks/Benefits
- N/A
Skills/Tech-stack
AI Evaluation | Chunking | Cost Optimization | Data Normalization | Data cleaning | Evaluation | Guardrails | Hybrid retrieval | LLMOps | Language Models | Large Language Models | Latency monitoring | Metadata filtering | Model Deployment | Optical character recognition | Prompt engineering | Prompt versioning | RAG | Re-ranking | Reliability Engineering | Retrieval-Augmented Generation | SQL | Schema-aware querying | Text-to-SQL | Unstructured Data | Vector Databases | Vector Search
Education
N/A
Roles
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