Feature Engineering Pipeline: Design, build and maintain reliable feature engineering pipelines on Databricks and AWS for ingesting, transforming, validating and serving structured and unstructured data used by ML/AI workloads.
Detection Engineering Agent Development: Develop AI agents that assess data feasibility, propose candidate risk indicators and detection features, and support the translation of validated risks into actionable analytics and detection logic.
Performance and Cost Engineering: Optimise Spark and SQL jobs, file layout, partitioning, cluster configuration and workload scheduling to improve reliability, runtime and AWS/Databricks cost efficiency.