Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Build the agentic-security surface. Partner on red-teaming and adversarial evaluation - tool-chain exploits, RBAC and permission-inheritance failures, multi-turn safety erosion - so we catch what a motivated adversary would.
What You'll Do: Design, implement, and deploy AI models and algorithms Analyze data to extract actionable insights Collaborate with cross-functional teams on AI projects Optimize machine learning pipelines for performance Ensure security and reliability of AI solutions Document processes and maintain code quality
Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Research and evaluate emerging AI technologies, including LLMs, AI agents, predictive analytics, fraud and anomaly detection, text analytics, and customer segmentation.