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Metro location and desirable AI/data skills with moderate brand recognition yield medium competition.
Requires AI-focused data engineering and vector infra skills, somewhat specialized yet reasonably transferable across tech industries.
Specific mandatory data engineering tooling and production AI experience required, but no explicit years, so medium strictness.
Design and maintain scalable data pipelines for Retrieval-Augmented Generation (RAG) converting unstructured IT logs into vector embeddings.
Manage health and performance of vector databases (e.g., Pinecone, Milvus, Weaviate) ensuring sub-second retrieval for AI reasoning.
Build and deploy CI/CD data infrastructure pipelines with automated data guardrails to ensure data quality, privacy, and up-to-date AI context windows.
Expertise in data mining, storage, and ETL processes with experience in data pipeline tools like Glue, Databricks, Synapse, or Dataproc.
Proficient in both relational and NoSQL databases, such as PostgreSQL, DB2, and MongoDB.
Excellent problem-solving and ability to translate technical requirements to non-technical stakeholders.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced data engineer familiar with cloud modernization and data modeling, able to architect sophisticated AI data infrastructure.
Demonstrates strong software engineering skills including CI/CD pipeline development and automated data quality control.
Has certifications in data engineering or cloud platforms (AWS, Azure, GCP) and comfortable working with social coding tools like GitHub and IDEs like Visual Studio.