Senior AI Data Engineer

Lead design, development, and optimization of scalable, cloud-native AI data platforms and pipelines supporting the entire AI lifecycle including training, inference, and continuous improvement.

Build and maintain production-grade data infrastructure enabling enterprise AI, including Generative AI, with emphasis on data quality, governance, security, and operational monitoring.

Collaborate with AI Engineers, Data Scientists, and Architects to deliver reusable data products and support MLOps and AgenticOps workflows for enterprise-wide AI innovation.

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 and machine learning workloads.

Strong proficiency in Python, SQL, Spark, distributed data processing, and cloud-native data platforms (AWS, Azure, or GCP).

Experience with data lakes, Lakehouse architectures, streaming technologies (Kafka, Kinesis, or Azure Event Hubs), workflow orchestration tools (e.g. Apache Airflow, Azure Data Factory), and enterprise data governance.

Experienced senior-level data engineer with proven ability to deliver secure, scalable, and high-performance AI data infrastructures at enterprise scale.

Deep domain knowledge in AI and Generative AI data engineering including feature stores, vector databases, embeddings, RAG, and semantic search to accelerate model development.

Strong operational focus enabling automation, monitoring, and continuous improvement of AI data pipelines within cloud environments, contributing to AI Center of Excellence objectives.