AI Risk Engineer Senior Associate 1

Overview

Join RSM's Technology Risk Consulting team as an AI Risk Engineer to assess AI governance, security, privacy, model performance, compliance and responsible AI risks, helping organizations adopt AI responsibly.

What you'll do

• Perform AI risk, AI governance, responsible AI, and AI assurance assessments across enterprise environments.
• Evaluate generative AI solutions, machine learning systems, AI agents, AI platforms, and third-party AI services for operational, security, privacy, regulatory, and ethical risks.
• Assess AI lifecycle processes, including data sourcing, model development, testing, validation, deployment, monitoring, change management, and retirement.
• Review controls related to bias, fairness, explainability, transparency, accountability, human oversight, model performance, security, and privacy.
• Analyze AI architectures, workflows, and technical implementations to identify risk exposures and practical mitigation strategies.
• Support enterprise AI inventory, use-case classification, risk assessment, approval, monitoring, and governance initiatives.
• Partner with Engineering, Product, Cybersecurity, Legal, Privacy, Compliance, Data, Risk, and business teams to evaluate AI risks and controls.
• Participate in client workshops and stakeholder interviews, translating technical AI concepts into clear business and risk implications.
• Prepare client-facing reports, risk assessments, executive summaries, and practical advisory recommendations.
• Contribute to AI risk methodologies, assessment accelerators, thought leadership, and global engagements delivered through onshore/offshore operating models.

What you'll need

• Bachelor's or master's degree in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Software Engineering, Applied Mathematics, Computational Statistics, Data Analytics, or a closely related technical discipline.
• 4+ years of experience across AI, machine learning, data science, AI engineering, MLOps, AI platform engineering, Trust & Safety, product risk, technology risk, cybersecurity, model risk management, data governance, or related technology disciplines.
• Demonstrable experience developing, supporting, evaluating, monitoring, governing, or assessing AI-enabled products, machine learning solutions, or generative AI use cases in production environments.
• Strong understanding of generative AI, large language models, AI agents, retrieval-augmented generation, model evaluation, AI lifecycle management, and responsible AI principles.
• Working knowledge of AI risks including bias, fairness, explainability, privacy, security, robustness, model drift, human oversight, and third-party dependencies.
• Experience working with global firms in cross-functional and geographically distributed teams, including onshore/offshore delivery models.
• Strong written and verbal communication skills, with the ability to explain technical concepts to technical, business, and executive stakeholders.
• Demonstrated analytical, problem-solving, consultative, documentation, and stakeholder management skills.

Nice to have

• Experience working for global technology companies, digital-first organizations, cloud providers, hyperscalers, AI product organizations, or large multinational enterprises.
• Experience supporting AI solutions at scale within production environments.
• Exposure to Microsoft Copilot, Azure AI, OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Databricks AI, Hugging Face, or similar enterprise AI technologies.
• Familiarity with NIST AI RMF, ISO/IEC 42001, responsible AI frameworks, AI Trust & Safety, AI governance, or AI assurance practices.
• Experience supporting AI governance programs, AI assurance reviews, AI safety assessments, model evaluations, third-party AI risk reviews, or model risk initiatives.
• AI or machine learning certification such as AIIA, AIGP, ISO/IEC 42001 Lead Implementer or Auditor, Microsoft Azure AI Engineer Associate, AWS Machine Learning, Google Professional Machine Learning Engineer, or equivalent.
• Working knowledge of AWS, Azure, or GCP and cloud-native AI services; exposure to cloud governance, IAM, cloud security, DevSecOps, Infrastructure as Code, or cloud resilience is an advantage.
• Prior experience within a Big 4, leading consulting organization, global technology company, or product engineering environment.

Details

• Location: Gurugram, India.
• Works within onshore/offshore operating models with geographically distributed teams.