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.
