Overview
On Site
Hybrid
BASED ON EXPERIENCE
Full Time
Skills
COMPLIANCE
EXPLAINABILITY
RESPONSIBLE AI
RISK CHECKS
Job Details
Role Overview
Lead responsible AI initiatives by ensuring ethical deployment, regulatory compliance, risk management, and model explainability across AI systems. Champion trustworthy and transparent AI practices. Responsibilities
- Develop and implement responsible AI frameworks, policies, and governance structures
- Conduct ethical assessments and risk evaluations for AI systems and models
- Ensure compliance with AI regulations (GDPR, AI Act, CCPA, industry standards)
- Perform bias audits, fairness testing, and discrimination risk assessments
- Implement model explainability and interpretability solutions (SHAP, LIME, attention visualization)
- Create documentation and transparency reports for AI systems
- Collaborate with legal, compliance, and technical teams on AI governance
- Train teams on responsible AI practices and ethical considerations
- Monitor emerging AI regulations and update policies accordingly
Requirements
- Bachelor's degree in Computer Science, Law, Ethics, Public Policy, or related field
- Strong understanding of responsible AI principles and frameworks
- Proven experience with AI compliance and regulatory requirements
- Expertise in risk assessment methodologies for AI systems
- Hands-on experience with explainability tools and techniques (SHAP, LIME, InterpretML)
- Knowledge of AI ethics, fairness metrics, and bias mitigation strategies
- Understanding of ML model development lifecycle and deployment practices
- Excellent communication skills to explain complex technical concepts to non-technical stakeholders
Preferred
- Advanced degree (MS/PhD) in relevant field or law degree with tech focus
- Experience with AI governance frameworks (NIST AI RMF, ISO/IEC standards, EU AI Act)
- Familiarity with industry-specific regulations (healthcare HIPAA, finance regulations)
- Technical background with Python and ML frameworks
- Experience conducting algorithmic impact assessments
- Knowledge of privacy-preserving techniques (differential privacy, federated learning)
- Certifications in AI ethics or governance
- Experience working with cross-functional and executive teams
Technical Skills
- AI explainability tools (SHAP, LIME, Captum, InterpretML)
- Understanding of ML models and their potential risks
- Documentation and audit trail management
- Risk assessment frameworks and methodologies
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