Applied Artificial Intelligence & Machine Learning- Markets Operations - Vice President

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

Lead the design and delivery of AI/ML solutions for Market Operations at JPMorgan Chase. This role involves managing a team, partnering with business and technology stakeholders, and ensuring the successful implementation and scaling of AI-driven tools. The focus is on developing and deploying production-grade AI/ML applications, establishing best practices for evaluation and monitoring, and driving measurable impact.

What you'd actually do

  1. Developing and Delivering AI Solutions: Lead the development, implementation and end-to-end delivery of advanced machine learning models and algorithms to drive market operations initiatives.
  2. Partner with Engineering to Enable Scalable AI/ML Architecture & Deployment : Partner with AI Engineering teams to ensure architecture design and deployment patterns are robust for AI/ML models and aligns with business needs
  3. Team Management & Mentorship following Agile Practices : Lead and mentor a team of AI/ML data scientists, guiding iterative experimentation and tracking progress to deliver measurable outcomes in an agile manner
  4. Maintaining Code Practices: Ensure production-grade AI/ML applications and tools are built, maintained, and optimized
  5. Model Evaluation and Monitoring : Establish best practices for designing robust architectures, evaluation, monitoring, governance and deployment

Skills

Required

  • MSc/PhD in Computer Science, Data Science, Machine Learning, or related field
  • Python
  • ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • data preprocessing
  • feature engineering
  • evaluation techniques
  • statistical analysis
  • cloud platforms (AWS, Azure, GCP)
  • containerization (Docker, Kubernetes)
  • managing data science teams
  • communication skills

Nice to have

  • prompt optimization frameworks (AutoPrompt, DSPy)
  • building evaluation suites
  • agentic frameworks (LangChain, LangGraph)
  • AWS AI deployment services (SageMaker, Bedrock)
  • workflow orchestration
  • financial services experience
  • investment banking operations experience

What the JD emphasized

  • Significant proven experience building and deploying AI applications in large scale production environments
  • Experience with MLOps practices and tools for managing the machine learning lifecycle
  • Experience managing data science teams and couching team members

Other signals

  • end-to-end delivery of advanced machine learning models
  • scaling of AI-driven tools
  • deliver measurable impact through innovative AI/ML applications
  • building and deploying AI applications in large scale production environments
  • MLOps practices and tools for managing the machine learning lifecycle