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Mid-level metro AI/data role with broad toolset requirements and visible title increases candidate competition.
Strong enterprise ITSM, CMDB, and RAG specialization limits cross-industry transferability, increasing domain sensitivity.
Explicit 5+ years, mandatory GenAI/RAG experience, and specific tech stack requirements enforce strict shortlisting.
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Build and maintain secure, AI-ready data pipelines integrating complex operational data from multiple enterprise sources (ServiceNow, SharePoint, Confluence, Git, monitoring tools).
Design and implement advanced semantic search architectures including vector storage, embedding, indexing, and metadata tagging to optimize AI agent retrieval performance.
Ensure data governance with strict access control, lineage tracking, compliance audit support, and provide APIs and context packages for AI agent integration.
5+ years experience in data engineering or platform development, with minimum 2 years supporting GenAI, RAG, semantic search, or related AI data products; ITSM/ServiceNow experience preferred.
Proficiency in Python, SQL, ETL/ELT pipelines; experience with orchestration platforms like Databricks, Snowflake, BigQuery, Airflow, or dbt.
Hands-on knowledge of LLM and RAG architectures including embeddings, vector databases (e.g., Azure AI Search, Pinecone, Elasticsearch) and AI frameworks like LangChain and LlamaIndex.
Strong expertise in enterprise data governance, lineage, access controls, and sensitive data handling for AI applications.
Experienced in integrating complex enterprise operational data sources into AI and retrieval platforms, especially in ITSM contexts.
Demonstrates advanced skills in designing and tuning high-accuracy retrieval augmented generation (RAG) systems and knowledge graphs for AI agents.
Proven track record of delivering AI-ready data pipelines with compliance and security rigor in large-scale enterprise environments.