Responsibilities:
Assess AI systems, machine learning models, and AI-powered applications against predefined benchmarks, performance thresholds, and responsible AI standards.
Evaluate AI systems for fairness, transparency, accountability, reliability, privacy, security, and potential ethical risks.
Design and implement technical mitigation strategies to address AI-related risks, including algorithmic bias, inaccurate outputs, model drift, and unintended system.
Develop and maintain responsible AI assessment frameworks, evaluation methodologies, testing procedures, and monitoring mechanisms.
Conduct AI risk assessments and impact analyses to identify potential risks throughout the AI development and deployment lifecycle.
Implement and validate responsible AI controls to ensure AI systems meet internal policies, applicable regulatory requirements, and ethical guidelines.
Collaborate with AI engineers, data scientists, machine learning engineers, software developers, cybersecurity teams, and governance stakeholders.
Establish measurable indicators, testing criteria, and performance metrics to monitor AI system reliability, fairness, robustness, and compliance.
Investigate AI model failures, unexpected outputs, and potential ethical concerns, and recommend corrective actions and continuous improvements.
Support the development of AI governance documentation, risk reports, technical assessments, and compliance evidence.
Monitor emerging responsible AI technologies, regulatory developments, industry standards, and best practices to improve AI governance and risk mitigation.
Contribute technical recommendations and solutions to strengthen responsible AI implementation across enterprise AI initiatives, particularly in telecommunications environments.
Requirements:
• Minimum Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, Software Engineering, or a related technical field.
• Minimum 5 years of relevant professional experience in AI engineering, machine learning, responsible AI, AI governance, model risk management, or a similar role.
• Experience assessing AI systems against predefined benchmarks, evaluation criteria, and responsible AI standards.
• Strong understanding of responsible AI principles, including fairness, transparency, explainability, accountability, privacy, safety, and reliability.
• Hands-on experience designing and implementing technical controls or mitigation strategies for AI-related risks.
• Knowledge of AI model evaluation, bias detection, robustness testing, model validation, and performance monitoring.
• Understanding of machine learning algorithms, AI model development, and the AI lifecycle from development to deployment and monitoring.
• Ability to translate ethical AI principles, risk assessment findings, and governance requirements into practical technical solutions.
Mandatory / Core Skills:
1. Responsible AI & AI Ethics: Understanding of responsible AI principles, ethical AI practices, fairness, transparency, explainability, and accountability.
2. AI Risk Assessment & Model Evaluation: Experience evaluating AI systems against defined benchmarks, performance thresholds, and risk criteria.
3. AI Risk Mitigation: Ability to design, implement, and validate technical controls to mitigate bias, reliability issues, and other AI-related risks.
4. Machine Learning & AI Systems: Strong understanding of AI/ML models, model validation, testing, and monitoring.
5. AI Governance & Compliance: Knowledge of AI governance frameworks, responsible AI policies, and applicable regulatory requirements.
Preferred / Good-to-Have Skills:
• Experience working in the telecommunications industry.
• Familiarity with responsible AI frameworks and standards, such as NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001, and ISO/IEC 23894.
• Experience with explainable AI (XAI), algorithmic fairness, bias detection, and model interpretability.
