• Led AI safety reviews for machine learning systems prior to production deployment across multiple product lines.
• Designed and executed fairness and bias audits to identify risks across demographic groups.
• Collaborated with ML engineers to implement model monitoring and alerting for safety-related performance issues.
• Developed internal AI governance policies covering model documentation, data usage, and accountability.
• Conducted stress testing and adversarial analysis to evaluate model robustness under edge cases.
• Partnered with legal and compliance teams to align AI practices with emerging regulations.
• Prepared safety assessment reports and presented findings to executive leadership.
• Mentored junior safety analysts and established standardized safety evaluation workflows.
• Supported incident response processes for AI-related failures and user impact analysis.

• Performed model risk assessments for AI systems used in healthcare, finance, and customer support applications.
• Analyzed training data for quality, representativeness, and potential sources of bias.
• Assisted in building explainability tools to improve transparency for internal stakeholders.
• Monitored deployed models for drift, performance degradation, and unintended behaviors.
• Documented model assumptions, limitations, and safety considerations for audits.
• Collaborated with product managers to integrate safety requirements into product roadmaps.
• Supported internal training sessions on responsible AI practices and risk awareness.
• Contributed to the development of internal guidelines for ethical AI deployment.
• Worked with QA teams to validate safety controls during model release cycles.

• Supported AI governance initiatives by tracking model inventories and documentation.
• Assisted senior specialists with data audits and validation checks.
• Helped evaluate AI use cases for potential ethical and operational risks.
• Prepared compliance documentation for internal reviews and external audits.
• Collaborated with engineering teams to understand system architecture and data flows.
• Analyzed user feedback and incident logs to identify emerging safety concerns.
• Maintained reporting dashboards for AI risk metrics.
• Supported policy research related to AI ethics and regulatory trends.

Contributed to explainability tools and documentation improving stakeholder trust in AI systems

Supported implementation of fairness testing processes that reduced demographic bias in model outputs

Led development of a structured risk assessment framework adopted across multiple AI products