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Tier-1 brand and metro location but specialized knowledge-graph/RAG skills limit applicant density.
Highly domain-specific skills (knowledge graphs, RAG, large-scale data platforms) restrict cross-industry transferability.
Explicit 8–14 years requirement, deep technical and leadership skills, and niche knowledge-graph/RAG expertise.
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Lead design and architecture of scalable, enterprise-grade AI data platforms, including data ingestion, transformation, storage, and retrieval systems.
Own architecture and standards for knowledge graphs, ontologies, semantic models, and metadata-driven AI data services across the platform.
Drive adoption of modern data engineering practices, establish governance, data contracts, lineage, and collaborate cross-functionally to integrate AI data systems.
8–14 years of experience in data engineering, data architecture, or large-scale data systems.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
Deep expertise with ETL/ELT frameworks, big data processing technologies (e.g., Spark, Databricks, Flink), knowledge graphs, RAG, and ontology design.
On-premise work location requirement explicitly mentioned.
Experienced technical leader with proven ability to influence enterprise architecture decisions and lead teams.
Strong background in designing AI-ready data platforms integrating data lakes, warehouses, and AI-serving layers at scale.
Skilled in modern data engineering practices including orchestration, CI/CD for data pipelines, observability, and data governance frameworks.