Responsibilities
• Define and execute comprehensive test strategies covering statistical, ML, LLM and agentic AI models.
• Perform functional, regression and scenario - based testing of model behaviours and workflows.
• Conduct AI/ML evaluations including accuracy checks, bias/fairness assessment, robustness analysis and drift detection.
• Assess end - to - end model workflows including data inputs, feature transformations, task completion, tool - use accuracy and multi - step reasoning.
• Design and maintain automated test and evaluation pipelines, including benchmarking and regression frameworks.
• Validate API and tool - integration behaviour in production - like environments, identifying dependency or orchestration issues.
• Diagnose issues using observability, logging, tracing and debugging tooling, and document findings clearly.
• Collaborate with data scientists across departments to understand modelling intent, feature logic and expected behaviours.
• Perform data - management tasks to support AI/ML model testing, including maintaining metadata, documenting key datasets and ensuring clarity of data inputs.
• Contribute to AI/ML proof - of - concept (POC) initiatives to strengthen evaluation methodologies and support innovation.
• Support data-management/analytics initiatives such as the Analytics Workbench and contribute to AI/ML/data analytics enablement.

Requirements
• Minimum 4 years of relevant experience in model testing, QA/QC, AI/ML evaluation, CI/CD, MLOps, data engineering, or related technical roles.
• Proficiency in Python (especially PySpark, MLlib, pytest), R and SQL; knowledge of Scala, Rust, Java, JS or C++ is a plus.
• Experience designing and executing test strategies for ML/AI models, including automated pipelines and regression frameworks.
• Ability to evaluate statistical, ML and LLM models using performance, bias, robustness and drift metrics.
• Strong ability to assess feature engineering logic, dataset integrity, workflow reliability and tool - integration behaviours.
• Experience troubleshooting using logs, traces and debugging tools to identify root - cause issues.
• Strong documentation and communication skills to articulate findings, risks and remediation requirements.
• Ability to collaborate effectively with data science, engineering, IT and governance functions.
• Understanding of Responsible AI concepts and quality expectations for production - ready AI/ML systems.