Qualifications: Bachelor’s or Master’s degree in Computer Science, Information/Cyber Security, AI/ML, Data Science, or related field
10-15+ years overall experience, including 3+ years in AI governance, AI runtime threat vectors and AI observability, monitoring, and drift management
Proven ability to design and govern runtime guardrails using AI governance and risk platforms (e.g., Credo.ai for AI inventory, policy enforcement, and risk assessment)
Strong hands‑on understanding of runtime monitoring and observability for AI systems, leveraging LLMOps/MLOps platforms such as MLflow, Weights & Biases, Datadog, Azure Monitor, CloudWatch, or equivalent, to track usage patterns, behavioral anomalies, and model drift in production.
Command of drift detection and AI behavior monitoring, including data drift, concept drift, and output instability, using observability and model monitoring tools (e.g., Aporia, Datatron, custom telemetry built on OpenTelemetry).
Ability to operationalize AI runtime governance controls within CI/CD and deployment pipelines, embedding security checks and enforcement into MLOps/LLMOps workflows orchestrated through platforms such as Kubeflow, Airflow, GitHub Actions, Azure DevOps, or Jenkins.
