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Mid-level role in a metro with common data/AI skills increases applicant competition.
Skills are transferable across industries but require specific Azure and RAG/vector search experience.
Explicit 4–8 years plus many mandatory Azure, Databricks, and RAG/vector skills increases filtering strictness.
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Design, develop, and maintain scalable Azure-based data ingestion pipelines and platforms enabling Generative AI, RAG, and intelligent automation.
Build and optimize document processing, vector search solutions, and AI-driven data workflows including Retrieval Augmented Generation (RAG) and Azure AI Search indexes.
Implement security (RBAC, Row-Level Security) and governance best practices while monitoring and tuning platform performance for reliability.
4 to 8 years of relevant experience in data engineering or AI data platforms.
Strong Python and SQL programming skills with experience in Azure Data Services (Databricks, Synapse, Data Factory, Azure Functions).
Hands-on experience building ETL/ELT data pipelines and working with Azure AI Search or similar platforms implementing RAG, embeddings, and vector search.
Bachelor's or Master's degree in Computer Science, Data Engineering, IT, or a related field. Certifications like Azure Data Engineer (DP-203) or Azure AI Engineer (AI-102) are preferred.
Has deep practical experience in enterprise data integration from diverse sources such as SharePoint, Azure DevOps, and databases within Azure cloud ecosystems.
Demonstrates strong operational focus on building AI-ready reliable data platforms supporting high-quality search and retrieval with measurable performance and security compliance.
Experience collaborating closely with AI teams on knowledge retrieval, AI automation solutions, and workflow orchestration using emerging tools like LangGraph, LangFlow, or Dagster.