Applied AI Ml-vice President

JPMorgan Chase JPMorgan Chase · Banking · Columbus, OH +1 · Consumer & Community Banking

JPMorgan Chase is seeking an Applied AI ML-Vice President to join their Consumer & Community Banking Machine Learning team. This role focuses on applying LLM-based methods to enhance, build, and deliver trusted market-leading technology products. Responsibilities include applying technical judgment, evaluating performance, ensuring solutions are reliable, secure, and scalable, improving data quality, monitoring models in production, and iterating to reduce agent effort and improve operational workflows. The role requires experience in building and scaling software/ML platforms, ML frameworks, ML Ops, AI Agents, tool integration, and RAG-based solutions.

What you'd actually do

  1. Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
  2. Develops secure and high-quality production code, and reviews and debugs code written by others
  3. Drives decisions that influence the product design, application functionality, and technical operations and processes
  4. Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
  5. Influences peers and project decision-makers to consider the use and application of leading-edge technologies

Skills

Required

  • BS in Computer science or similar fields with 5+ years experience or MS in Computer science or similar fields with 3+ years experience
  • LLM/NLP and search
  • building and scaling software and or machine learning platforms in high-growth or enterprise environments
  • machine learning frameworks
  • ML Ops tools and practices
  • engineering programming languages (e.g., Python, Java)
  • infrastructure as code (e.g., Terraform, CloudFormation)
  • building AI Agents (e.g., LangChain, LangGraph, AutoGen)
  • integration of tools (e.g., API)
  • RAG based solutions (e.g., open search)
  • software development pipeline and orchestration tools (e.g., Jenkins, GitLab CI/CD)

Nice to have

  • developing large-scale machine learning solutions based on big data to solve real world problems (e.g. Classification, Regression, or Recommender Systems)

What the JD emphasized

  • building and scaling software and or machine learning platforms in high-growth or enterprise environments
  • AI Agents
  • integration of tools
  • RAG based solutions

Other signals

  • LLM-based methods
  • reliable, secure, and scalable
  • agent effort
  • resolution times
  • consistency and quality
  • building and scaling software and or machine learning platforms
  • AI Agents
  • integration of tools
  • RAG based solutions