Ai/ml Engineer [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Corporate Sector

AI/ML Engineer at JPMorgan Chase focused on building AI-powered chatbots using cloud-based LLMs (AWS Bedrock Claude Sonnet, OpenAI GPT) to support customer inquiries, contributing to the product's journey toward Agentic AI. The role also involves developing storage management platforms, capacity management tools, and automation for infrastructure.

What you'd actually do

  1. Build and deploy AI-powered chatbots using cloud-based models to support customer inquiries related to billing and usage, advancing the product's journey toward Agentic AI.
  2. Develop dashboards and tools for intelligent capacity management enabling predictive analytics and actionable insights for monitoring, forecasting, and optimizing storage resources.
  3. Implement automation for storage builds, minimizing manual toil, and advancing product capabilities.
  4. Design and implement Python-based solutions to identify and reduce resource allocation for unused storage volumes, resulting in measurable cost savings and improved operational efficiency.
  5. Engage directly with customers to educate, gather feedback, and drive adoption of new features and self-service tools, ensuring solutions meet operational needs and improve user satisfaction.

Skills

Required

  • Developing and maintaining web-based management platforms for storage and infrastructure products including private cloud storage
  • building dashboards and tools using Tableau or Grafana for capacity management including predictive analytics and data visualization
  • designing and developing user interfaces using HTML, CSS, and JavaScript
  • automating infrastructure and application deployment processes using Python and containerization technologies including Kubernetes
  • developing and executing end-to-end performance testing strategies using Blazemeter, JMeter, and Locust
  • developing RESTful APIs using Django and FastAPI frameworks
  • using Django ORM and FastAPI dependency injection for database operations and business logic
  • developing web applications using React and Angular frameworks
  • Building and deploying AI-powered large language model use cases with cloud-based machine learning models including AWS bedrock Claude sonnet and OpenAI GPT
  • Working with Oracle SQL, PostgreSQL, and MongoDB to maintain and operate relational and non-relational databases
  • using GitHub and Bitbucket for version control and Continuous development
  • deploying Web Applications on AWS
  • using Docker and Kubernetes to build and containerize web applications and micro-services
  • Using Apache Airflow and Celery to schedule and trigger cron jobs

What the JD emphasized

  • Building and deploying AI-powered large language model use cases with cloud-based machine learning models including AWS bedrock Claude sonnet and OpenAI GPT

Other signals

  • AI-powered chatbots
  • Agentic AI
  • cloud-based models
  • large language model use cases