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Tier-1 brand, popular data engineering role, metro location drive high competition.
AI-focused data engineering skills are transferable but require specialized tooling knowledge.
Extensive mandatory cloud, streaming, MLOps, vector DB, and governance requirements indicate high filtering.
Lead design, development, and optimization of scalable, cloud-native data platforms and pipelines for enterprise AI and Generative AI solutions.
Build production-grade data infrastructure supporting AI lifecycle stages including data ingestion, feature engineering, model training, inference, and continuous improvement.
Ensure data quality, governance, security, and operational excellence in collaboration with AI engineers, data scientists, and architects.
Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field.
Significant experience designing and implementing enterprise-scale data engineering solutions supporting AI and machine learning workloads.
Strong proficiency in Python, SQL, Spark, distributed data processing frameworks, and cloud platforms (AWS, Azure, or Google Cloud).
Experience with streaming technologies (Kafka, Kinesis, Azure Event Hubs), workflow orchestration tools (Apache Airflow, Azure Data Factory), and data governance practices.
Experienced in building cloud-native, scalable AI data platforms with emphasis on production readiness and operational excellence.
Skilled at enabling AI lifecycle support including feature stores, vector databases, embeddings, RAG, and MLOps/AgenticOps pipelines.
Capable of collaborating across AI engineering, data science, and platform teams, ensuring secure, reliable, high-performance enterprise data solutions.