Join EY as a Manager in Business Consulting, specializing in Risk within the TMT sector, focusing on Digital Risk. This role is based in Mumbai. We are a global leader dedicated to building a better working world by fostering a culture of growth, opportunity, and creativity. At EY, we invest in your development, believing your career is yours to build with limitless potential and motivating experiences.

The TMT industry is undergoing rapid transformation, presenting both opportunities and challenges in competitiveness and agile strategy. We empower TMT companies to enhance customer and employee experiences, achieve operational excellence, and protect their brand. We also enable M&A strategies that drive value and position companies as leaders in the future technology revolution.

As a Manager in AI Risk and Governance, you will lead technical excellence by identifying, assessing, and mitigating risks across Responsible AI dimensions such as bias, fairness, robustness, explainability, privacy, and model/data security. You will perform quantitative AI model testing, conduct AI red teaming, and design Responsible AI frameworks. Key responsibilities include building automated AI validation pipelines, implementing model interpretability solutions, defining RAI metrics, and operationalizing AI governance processes.

You will also configure and deploy AI systems using MLOps, LLMOps, and DevOps practices, collaborating with cybersecurity and fraud teams to address AI-driven risks. Translating AI regulations into technical requirements and communicating AI risks to stakeholders are also crucial aspects of this role. You will develop technical documentation and training materials to support widespread Responsible 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 building, testing, deploying, and monitoring AI/ML systems in production is essential, along with a strong understanding of Responsible AI risks.

Your qualifications should include experience with quantitative AI testing, MLOps/LLMOps/DevOps, and the ability to translate regulatory requirements into technical controls. Excellent communication skills, including experience with cross-functional or client-facing teams, are vital. Familiarity with AI red teaming and collaboration with enterprise risk teams is preferred. Experience with agentic AI systems and associated risks is a plus. Preferred certifications include Azure AI Engineer Associate, AWS Machine Learning – Specialty, Google Professional Machine Learning Engineer, CISSP, CCSP, or CRISC.