AI Controls Vice President

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Commercial & Investment Bank

This role focuses on managing AI/ML risk and strengthening controls for AI use cases, particularly GenAI and agentic AI, within a financial services context. The individual will lead technical AI risk assessments, develop governance frameworks, and ensure responsible adoption at scale, translating complex AI concepts into actionable governance and control standards.

What you'd actually do

  1. Drive technical AI risk assessments by evaluating model design, data quality, bias, drift, and failure modes.
  2. Lead cross-functional teams to develop and implement AI governance frameworks, policies, and control standards.
  3. Own deep-dive technical reviews to assess accuracy, fairness, explainability, security, and resiliency risks.
  4. Translate AI/ML concepts into clear risk assessments and recommendations for senior and non-technical stakeholders.
  5. Leverage Python, AI tools, and GenAI/LLMs to automate controls, strengthen evidence, and improve risk management.

Skills

Required

  • 8+ years of experience working with AI/ML technologies, data science, or related technical fields.
  • Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or similar field.
  • Strong Python proficiency for analytics, automation, and scalable control solutions.
  • Strong understanding of ML concepts and the full model development lifecycle.
  • Evaluate AI/ML models for bias, overfitting, data quality issues, drift, and validation gaps.
  • Lead teams and influence senior stakeholders to deliver strategic initiatives and outcomes.
  • Communicate clearly by explaining technical AI concepts to non-technical audiences and executives.

Nice to have

  • Hands-on experience with GenAI, LLMs, or agentic AI systems.
  • Prior exposure to risk management, controls, compliance, or audit functions.
  • Knowledge of financial services, AML/KYC processes, and regulatory environments.
  • Implement AI governance frameworks, model risk management, or responsible AI principles.
  • Execute model validation, monitoring, or AI quality assurance practices.
  • Build API integrations and data pipelines to support scalable monitoring and evidence collection.
  • Drive innovation by designing strategic solutions for control issues in fast-paced environments.

What the JD emphasized

  • AI/ML risk management
  • responsible GenAI and agentic AI adoption at scale
  • AI governance frameworks, policies, and control standards
  • technical AI risk assessments
  • model risk management
  • responsible AI principles

Other signals

  • AI risk management
  • AI governance
  • responsible AI adoption
  • GenAI and agentic AI