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THE CHANCE Curate and govern the context layer (RAG/KBs, embeddings, metadata, labeling) to improve answer quality and minimize hallucinations, while protecting data/PII.
RESPONSIBILITIES
Curation & Labeling
- Extract and curate content from enterprise sources (Confluence, Jira, SharePoint, ServiceNow, qTest) using APIs and automation.
- Define chunking and metadata schemas; labeling guidelines; golden Q&A; and evaluation sets.
- Implement chunking strategies for diverse content types (code repositories, technical documentation, tickets, test cases).
- Implement curation workflows and retention policies.
Retrieval Quality
- Run A/B experiments across vector stores; monitor answer quality vs. cost/latency; recommend defaults.
- Analyze failure cases and propose data-driven improvements.
Data Governance
- Enforce data minimization, retention, and access controls; maintain lineage and approvals per RAI (Responsible AI).
- Document data sources and usage for audit readiness.
SKILLS & QUALIFICATIONS
Required
- 3+ years data/ML experience with embeddings/retrieval expertise; strong documentation and runbook skills.
- Experience with content transformation, metadata extraction, and labeling workflows.
- Familiarity with privacy and data governance principles.
- Hands-on experience with vector stores (OpenSearch/pgvector/Kendra/Chroma) and labeling tools.
- Experience with REST APIs and data extraction from enterprise systems.
- Python coding proficiency for data pipelines and automation.
Preferred/Nice to have
- Experience designing golden datasets and evaluation pipelines.
- AWS Bedrock Knowledge Bases experience.
- Familiarity with software development lifecycle and technical documentation patterns.