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Niche RAG/vector-database expertise narrows applicant pool despite Bengaluru metro demand.
Core data engineering skills are broadly transferable, though RAG/vector DB specialization increases domain specificity.
Explicit 6+ years, mandatory data engineering skills, vector DB and cloud tooling create strict shortlisting.
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Architect and build scalable ETL/ELT data pipelines for batch and real-time processing across hybrid cloud environments.
Manage and optimize vector databases and semantic layers to enable fast, context-rich AI search and Retrieval-Augmented Generation (RAG).
Develop and maintain APIs and CI/CD pipelines to ensure reliable, secure data access and continuous deployment of data infrastructure.
6-8+ years of experience in software engineering or AI Data solutions with enterprise-grade implementations and customer-facing consulting exposure.
Bachelor's degree in Computer Science, Software Engineering, or related field (or equivalent experience).
Strong programming skills in SQL and Python; experience with ETL/ELT tools (Airflow, dbt, Kafka) and data pipeline tooling (Glue, Databricks, Synapse, Dataproc).
Experience with relational and NoSQL databases (PostgreSQL, DB2, MongoDB) and cloud platforms (AWS, Azure, GCP).
Experienced in designing and scaling data infrastructure for AI applications, specifically in vector embeddings and semantic knowledge layers.
Able to lead collaborative, cross-functional teams translating business needs into data architecture within rapid innovation cycles.
Strong expertise in data governance, distributed systems, and performance optimization in hybrid cloud environments.