AI Agent Engineer

Design, develop and deploy AI agents on Databricks, owning the full lifecycle from prototyping to production – including versioning, monitoring, and maintenance of multiple agents running in parallel. Build and maintain robust evaluation frameworks using MLflow's evaluation suite, implementing and customizing judges to assess agent quality across correctness, safety, and business-specific criteria. Drive continuous quality improvement of AI agents through structured offline evaluation pipelines and online production monitoring, leveraging MLflow tracing to understand agent execution patterns. Collect, structure and incorporate human feedback from diverse stakeholder groups into evaluation workflows and agent improvement cycles. Collaborate with Data Engineers, IT, and R&D to define data configurations and pipelines feeding AI agents with reliable inputs. Leverage and combine multiple data sources (on-site, off-site, connected vehicle data) to build agents that identify trends, anomalies, and quality signals at scale. Surface agent outputs and quality insights to Quality business stakeholders through dashboards and visual reporting tools, translating complex model behavior into actionable information. Identify and close data quality gaps, ensuring data feeding into agents is clean, consistent, and continuously improving through well-defined requirements and automated checks.