AI Ethics Specialist - Job DescriptionJob Title AI Ethics Specialist
Location [City/Remote/Hybrid]
Employment Type Full-time / Contract
Job Summary We are seeking an AI Ethics Specialist to ensure that artificial intelligence systems are designed, developed, deployed, and governed responsibly. This role will work closely with data scientists, AI engineers, legal teams, compliance professionals, product managers, and business stakeholders to identify ethical risks, establish governance frameworks, and promote fairness, transparency, accountability, privacy, and regulatory compliance across AI initiatives.
Key Responsibilities - Develop and implement responsible AI policies, standards, and governance frameworks.
- Assess AI systems for ethical, legal, and societal risks throughout the AI lifecycle.
- Conduct AI ethics reviews, algorithmic impact assessments, and risk assessments.
- Evaluate AI models for fairness, bias, explainability, transparency, robustness, and accountability.
- Collaborate with AI development teams to embed ethical principles into solution design.
- Ensure compliance with applicable AI regulations, privacy laws, and organizational policies.
- Establish processes for AI governance, model documentation, and audit readiness.
- Advise stakeholders on responsible AI practices and ethical decision-making.
- Develop AI ethics guidelines, training materials, and awareness programs.
- Monitor emerging AI regulations, standards, and industry best practices.
- Support incident response and remediation related to AI risks or ethical concerns.
- Participate in cross-functional governance committees and ethics review boards.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Law, Public Policy, Ethics, Information Systems, or a related field.
- 3-8+ years of experience in AI governance, AI ethics, responsible AI, risk management, compliance, or related areas.
- Strong understanding of machine learning, generative AI, and the AI development lifecycle.
- Knowledge of fairness, bias mitigation, explainability, privacy, transparency, and accountability concepts.
- Familiarity with AI risk management frameworks and model governance practices.
- Understanding of AI regulations, data protection laws, and industry standards.
- Excellent analytical, communication, and stakeholder management skills.
Preferred Qualifications - Experience implementing Responsible AI programs within an enterprise environment.
- Familiarity with AI governance tools and model monitoring platforms.
- Knowledge of privacy-enhancing technologies and secure AI development practices.
- Professional certifications in AI governance, privacy, cybersecurity, risk management, or compliance.
- Experience working with multidisciplinary teams across legal, compliance, technology, and business functions.
Technical Skills - AI Governance
- Responsible AI
- AI Risk Management
- Algorithmic Impact Assessment
- Model Governance
- Fairness Evaluation
- Bias Detection and Mitigation
- Explainable AI (XAI)
- Model Documentation
- Data Privacy
- Generative AI Governance
- Prompt Risk Assessment
- AI Security Fundamentals
- Python (preferred)
- SQL (preferred)
- Data Analysis
- Risk Assessment Methodologies
Soft Skills - Ethical decision-making
- Critical thinking
- Problem-solving
- Stakeholder management
- Communication and presentation
- Policy development
- Cross-functional collaboration
- Attention to detail
- Strategic thinking
Key Deliverables - Responsible AI policies and governance frameworks
- AI ethics assessment reports
- AI risk registers
- Algorithmic impact assessments
- Bias and fairness evaluation reports
- Model governance documentation
- AI compliance recommendations
- Training materials and awareness programs
- Audit and regulatory readiness documentation
Success Metrics - Compliance with AI governance policies and applicable regulations
- Reduction in AI-related ethical and operational risks
- Successful completion of AI ethics assessments
- Improved fairness, transparency, and explainability of AI systems
- Timely identification and mitigation of AI risks
- Positive audit outcomes and regulatory readiness
- Increased organizational adoption of responsible AI practices
