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Job Description
THE OPPORTUNITY
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.