Join a leading global professional services organization as a Senior Consultant specializing in Digital Risk within the Technology, Media, and Telecommunications (TMT) sector. This role offers a dynamic environment where you'll help transform businesses through cutting-edge technology and innovation. You'll be instrumental in solving complex strategic problems for clients, contributing to a better working world by managing risks and driving long-term value.

We are dedicated to fostering a culture of continuous learning and growth, providing unparalleled training and opportunities. At EY, your career journey is yours to build, with limitless potential for professional development and impactful experiences.

As a Senior Consultant in AI Risk and Governance, you will identify, assess, and mitigate technical risks related to bias, fairness, robustness, explainability, privacy, and security in AI models. You will conduct quantitative AI model testing, perform AI red teaming assessments, and design/implement Responsible AI frameworks. Key tasks include building automated AI validation pipelines, implementing model interpretability solutions, defining RAI metrics, and operationalizing AI governance processes for model lifecycle management.

Further responsibilities involve configuring and monitoring AI systems using MLOps, LLMOps, and DevOps practices, collaborating with cybersecurity and fraud teams, and translating AI regulations into technical controls. You will communicate AI risks and impacts effectively to stakeholders and develop training materials to support AI adoption.

To excel in this role, you should possess a Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field. We require 4-7 years of experience in AI/ML engineering, data science, AI risk, or AI governance. Hands-on experience with building, testing, deploying, and monitoring AI/ML systems in production is essential, along with a strong understanding of Responsible AI risks and quantitative AI testing frameworks.

Familiarity with MLOps/LLMOps/DevOps, CI/CD workflows for ML, and the ability to translate regulatory requirements into technical controls are crucial. Excellent communication skills and experience working with cross-functional or client-facing teams are expected. Experience with AI red teaming or safety assessments, and collaboration with cybersecurity or enterprise risk teams is highly beneficial.