AI Controls Manager Associate

JPMorgan Chase JPMorgan Chase · Banking · Hyderabad, Telangana, India · Commercial & Investment Bank

This role focuses on building and productionizing AI/ML solutions to enhance risk detection, continuous monitoring, and regulatory compliance within financial services (Client Onboarding & Documentation). It involves leading strategic control testing, automating processes, and applying GenAI for improved evidence gathering and analysis, with a strong emphasis on compliance and auditability in a regulated environment.

What you'd actually do

  1. Lead strategic control testing engagements using analytics to surface control gaps and BAU breaks; drive sustainable remediation.
  2. Execute strategic testing as an individual contributor with disciplined methods and clear evidence standards.
  3. Build and productionize AI/ML solutions for classification, anomaly detection, risk scoring, and entity resolution.
  4. Apply GenAI workflows to improve evidence gathering, document understanding, and root-cause analysis.
  5. Automate end-to-end testing and controls using Alteryx, Power BI/Tableau, and workflow/RPA platforms.

Skills

Required

  • Python or R for data manipulation, model development, and testing automation
  • modern ML and advanced analytics techniques including regression, classification, clustering, and dimensionality reduction
  • Integrate data sources using APIs
  • strong data engineering practices
  • data governance principles including data quality controls, metadata, lineage, and secure usage standards
  • Partner effectively across business, operations, and technology
  • critical thinking

Nice to have

  • LLMs/GenAI, predictive modeling, and their limitations in regulated environments
  • control testing knowledge including control design and root-cause analysis capability
  • Manage multiple automation initiatives concurrently
  • Communicate trends and systemic issues through high-quality executive summaries and governance updates
  • Implement monitoring for model drift, accuracy, and business KPIs with clear evaluation frameworks
  • explainability and auditability patterns to support regulatory and internal audit reviews
  • delivering reusable assets, templates, and standardized testing routines

What the JD emphasized

  • strong compliance and ethics
  • regulatory compliance
  • audit-ready evidence and reporting
  • regulatory expectations
  • model risk governance
  • regulated environments

Other signals

  • AI/ML solutions for classification, anomaly detection, risk scoring, and entity resolution
  • GenAI workflows to improve evidence gathering, document understanding, and root-cause analysis
  • Automate end-to-end testing and controls using Alteryx, Power BI/Tableau, and workflow/RPA platforms
  • Develop statistical tests and ML-based monitors for policy, legal, and regulatory compliance