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Tier-1 brand, metro location, senior data-engineer title, and broad AI/data stack create high competition.
AI data engineering skills are transferable, though enterprise governance and compliance increase domain specificity.
Extensive mandatory tech stack and domain experience for enterprise AI pipelines enforce high shortlisting strictness.
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Lead design and development of scalable, cloud-native data platforms and pipelines for AI, ML, and Generative AI solutions.
Own production-grade data infrastructure covering data ingestion, feature engineering, model training, inference, and continuous improvement.
Ensure data quality, governance, security, and operational excellence for enterprise AI initiatives.
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 for AI/ML workloads.
Proficiency in Python, SQL, Spark, distributed data processing, and cloud platforms (AWS, Azure, or GCP).
Experience with streaming technologies (Kafka, Kinesis, or Azure Event Hubs) and workflow orchestration tools (Apache Airflow, Azure Data Factory) required.
Experienced in building data architectures supporting large language models, vector databases, semantic search, and retrieval-augmented generation (RAG).
Strong background in data governance, metadata management, and operationalizing production data pipelines for AI applications.
Skilled in implementing MLOps/AgenticOps capabilities and modern data engineering best practices in cloud environments.