Responsibilities Build and contribute to the AI context platform Implement end‑to‑end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving Build and maintain patterns for incremental refresh, backfills, re‑embeddings, deduplication, and lineage across unstructured sources Contribute to retrieval quality improvements (query strategies, hybrid search, metadata filtering) in partnership with AI engineers Deliver semantic and governed data products Implement semantic layers (metrics/entities) that power BI and agent reasoning consistently Ensure datasets and indexes are documented and reusable Support reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident response Requirements BA or BS required, preferably in Computer Science, Engineering, or a technology‑based discipline 3–6 years in data engineering or data platform roles with strong hands‑on delivery Strong SQL and Python (or Scala/Java); solid production engineering habits Experience designing and operating cloud data pipelines at scale Experience working with unstructured data processing and search/retrieval concepts #J-18808-Ljbffr
