Applied AI ML Engineer Associate

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

Applied AI ML Engineer Associate at JPMorgan Chase within the Consumer & Community Banking Machine Learning team. The role focuses on applying LLM-based methods, building and scaling software/ML platforms, and developing AI agents with tool integration and RAG solutions. Responsibilities include driving innovation, architecting ML models, ensuring scalability, and promoting quality and security in production environments.

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

  • LLM/NLP
  • search
  • building and scaling software and or machine learning platforms
  • machine learning frameworks
  • ML Ops tools and practices
  • Python
  • Java
  • Terraform
  • CloudFormation
  • AI Agents
  • LangChain
  • LangGraph
  • AutoGen
  • API integration
  • RAG based solutions
  • open search
  • software development pipeline
  • orchestration tools
  • Jenkins
  • GitLab CI/CD

Nice to have

  • developing large-scale machine learning solutions based on big data
  • Classification
  • Regression
  • Recommender Systems

What the JD emphasized

  • building and scaling software and or machine learning platforms
  • AI Agents
  • integration of tools
  • RAG based solutions

Other signals

  • LLM-based methods
  • reliable, secure, and scalable
  • monitoring models in production
  • iterating to reduce agent effort
  • shorten resolution times
  • increase consistency and quality
  • building and scaling software and or machine learning platforms
  • ML Ops tools and practices
  • AI Agents
  • integration of tools
  • RAG based solutions
  • software development pipeline and orchestration tools