AVP/VP, AI/ML Model Validation Engineer

AVP/VP, AI/ML Model Validation Engineer

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.