Principal Applied AI ML Engineer

JPMorgan Chase JPMorgan Chase · Banking · Seattle, WA +1 · Corporate Sector

Principal AI/ML Engineer at JPMorgan Chase focused on building and operating LLM-powered APIs and agentic AI systems. The role involves designing agent architectures, writing production code, creating reusable components, managing the end-to-end ML lifecycle, optimizing inference, and embedding responsible AI practices. Requires strong Python, LLM/agentic AI experience, MLOps, and cloud deployment skills.

What you'd actually do

  1. Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses.
  2. Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code
  3. Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores
  4. Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows
  5. Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback

Skills

Required

  • Python engineering
  • PyTorch or TensorFlow
  • Vector storage systems
  • Agent memory design
  • Autonomous agent development
  • Tool use
  • Human-in-the-loop systems
  • LLM-backed service deployment
  • MLOps
  • CI/CD
  • Monitoring
  • Incident response
  • Model governance
  • Cloud-native AI deployment
  • AWS or Azure
  • Cost and performance optimization
  • Reusable platform development
  • Inference optimization
  • ML system performance optimization

Nice to have

  • fine-tuning
  • adapters
  • custom evaluation frameworks
  • prompt engineering
  • LLM orchestration
  • safety filters
  • audit logging
  • explainability in production systems
  • mentoring senior engineers
  • leading architecture discussions
  • influencing technical roadmaps and priorities

What the JD emphasized

  • 10 years of experience building applied machine learning systems, with recent hands-on work in LLMs or agentic AI
  • Expertise working with Vector storage systems and designing memory for Agents
  • Expertise developing long running agents that run autonomously using tools, skills and human in the loop
  • Proven experience deploying LLM-backed services to production (APIs, microservices)
  • Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance
  • Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization
  • Experience creating reusable platforms and patterns that accelerate delivery
  • Experience optimizing inference and ML system performance

Other signals

  • leading multiple complex ML projects
  • hiring, leading, and mentoring a team
  • reviewing code, mentoring engineers, troubleshooting production ML applications
  • rapid prototyping
  • parallel distributed computing, big data, cloud engineering, micro-services, automation, and operational excellence