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Tier-1 brand, metro location, and broad senior AI/data skill requirements increase applicant competition density.
Core AI data engineering skills transfer across industries, though payments compliance and enterprise context add some specificity.
Extensive mandatory tech stack, enterprise AI experience, and security/compliance expectations imply high filter strictness.
Design, build, and maintain scalable, cloud-native data platforms and pipelines to support AI, machine learning, and Generative AI solutions across the enterprise.
Lead development of data architectures including LLMs, vector databases, embeddings, and AI knowledge repositories supporting the full AI lifecycle from data ingestion to continuous model improvement.
Ensure data governance, security, quality, and operational excellence by implementing monitoring, validation, lineage, and automation for production-grade AI data infrastructure.
Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field.
Significant experience in enterprise-scale data engineering solutions supporting AI and machine learning workloads.
Proficiency in Python, SQL, Spark; experience with cloud-native platforms (AWS, Azure, or Google Cloud), streaming technologies (Kafka, Kinesis, or Azure Event Hubs), and workflow orchestration tools (Apache Airflow, Azure Data Factory, or similar).
Experience with data lakes, Lakehouse architectures, data warehouses, vector databases, feature engineering, and AI/Generative AI data support.
Experienced in designing and delivering secure, high-performance, and scalable AI data platforms within large enterprise environments.
Skilled in advanced AI data engineering practices including feature stores, metadata services, data API development, and monitoring/observability for AI pipelines.
Able to collaborate cross-functionally with AI Engineers, Data Scientists, and architects to operationalize AI/Generative AI solutions with strong governance and reliability focus.