AVP/VP, AI/ML Model Validation Engineer, Data Management Office (Singapore)

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.