Join EY as a Senior Consultant in Business Consulting Risk, specializing in Digital Risk within the TMT sector. This role offers an opportunity to shape the future of technology, media, and telecommunications companies by addressing critical digital risks. EY provides a supportive culture focused on training, opportunities, and creative freedom, enabling limitless career growth and fulfilling professional development.

This position is crucial for transforming businesses through innovation and technology. You will work with clients to navigate complex challenges in the TMT industry, helping them achieve operational excellence, protect their data and brand, and drive value through strategic M&A. Become a key player in building a better working world.

Identify and mitigate technical risks across Responsible AI dimensions such as bias, fairness, robustness, explainability, privacy, and security. Conduct quantitative AI model testing and AI red teaming assessments to uncover safety, security, and adversarial risks. Design and implement frameworks for bias detection, fairness evaluation, and model robustness. Build and maintain automated AI validation, monitoring, and alerting pipelines. Implement model interpretability solutions and define operational RAI metrics and dashboards.

Operationalize AI governance processes for model lifecycle management. Configure and monitor AI systems using MLOps, LLMOps, and DevOps practices. Collaborate with cybersecurity, fraud, and information security teams on AI-driven risks. Translate AI regulations and ethical standards into technical requirements and controls. Communicate AI risks and business impact clearly to stakeholders and develop training materials for Responsible AI adoption.

A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field is required. You should have 4–7 years of experience in AI/ML engineering, data science, AI risk, or AI governance. Demonstrable hands-on experience in building, testing, deploying, and monitoring AI/ML systems in production is essential.

Strong understanding of Responsible AI risks, including bias, fairness, robustness, explainability, privacy, and security, is necessary. Experience with quantitative AI testing, validation frameworks, MLOps/LLMOps/DevOps, and CI/CD workflows is required. The ability to translate regulatory requirements into technical controls and strong communication skills for cross-functional collaboration are key. Familiarity with agentic AI systems is a plus. Preferred certifications include Azure AI Engineer, AWS Machine Learning – Specialty, Google Professional Machine Learning Engineer, CISSP, CCSP, or CRISC.