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Popular Data Engineer title, metro location, and broad skillset increase applicant competition.
Core data engineering skills are transferable, but RAG/vector and observability focus adds domain specificity.
Multiple mandatory technical skills (ELK, Python, ETL, databases) imply medium strictness.
Design and scale data pipelines for Retrieval-Augmented Generation (RAG) using unstructured IT logs and documentation to create optimized vector embeddings.
Manage and ensure performance of vector databases (e.g., Pinecone, Milvus, Weaviate) to deliver sub-second retrieval speeds for AI reasoning processes.
Develop, deploy, and maintain CI/CD pipelines for data infrastructure, implementing semantic layers, knowledge graphs and automated data guardrails for data quality and safety.
Expertise in ELK stack (ElasticSearch, Logstash, Kibana).
Proficiency in Python programming.
Experience with data mining, data storage, ETL processes, data pipeline development, and tooling.
Work Experience Required: Not explicitly mentioned in the JD.
Strong background in data engineering focusing on AI applications, specifically experience building and scaling AI data infrastructure.
Experience with both relational (PostgreSQL, DB2) and NoSQL (MongoDB) databases and vector database management.
Ability to manage multiple projects involving complex data transformation and pipeline orchestration in production environments, with a methodical engineering approach.