Senior Lead Software Engineer, AI/ML Platform

JPMorgan Chase

Confirmed live 2 days ago Trusted

Quick summary

Work type
On-site
Location
Wilmington, DE
Posted
29 days ago
Freshness
Confirmed live 2 days ago

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How this pay compares to similar roles

Similar $203k
$148k most similar roles pay here $274k

This listing doesn't post a salary. Most similar roles pay $160,150–$246,150.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Senior Lead Software Engineer, AI/ML Platform

As a Senior Lead Software Engineer- AI/ML Platform within the Corporate - AIML Data Platforms team, you will design, build, and operate foundational cloud infrastructure to enable data scientists and machine learning engineers to develop, train, and deploy intelligent solutions. You will serve as a technical leader building reusable platform infrastructure, shared services, and automated tools for model deployment and inference. Your daily work involves managing GPU-intensive workloads, creating production-grade APIs, and optimizing platform reliability through orchestration and hardware acceleration. You will utilize Python, Java, or Go to develop infrastructure-as-code using Terraform within Docker and Kubernetes environments on AWS. The role focuses on the technical challenge of scaling AI/ML capabilities, including LLM operationalization, fine-tuning workflows, and establishing robust MLOps pipelines while ensuring secure, high-performance production environments for complex machine learning lifecycle management.

What you'll do

  • Build and maintain reusable AI/ML platform infrastructure and shared services to support large-scale development and deployment.
  • Architect, deploy, and operate secure cloud and container-based environments for training and inference, including GPU-intensive workloads.
  • Design and implement infrastructure-as-code solutions and automation tools to streamline model deployment and environment provisioning.
  • Develop production-grade services, APIs, and SDKs to support the full AI application lifecycle management.
  • Optimize platform reliability, scalability, latency, and cost through orchestration, scheduling, and hardware acceleration.
  • Establish operational best practices for monitoring, logging, observability, access controls, and incident response.
  • Support enterprise LLM operationalization, including fine-tuning workflows, inference optimization, and evaluation.
  • Drive the adoption of AI-assisted engineering practices to improve code quality and delivery speed across teams.

What we're looking for

  • Formal training or certification in software engineering concepts and 5+ years of applied experience.
  • Experience delivering secure, production-quality code in Python or Java.
  • Strong foundations in distributed systems, microservices, and platform architecture/design principles.
  • Proven ability to architect and operate cloud-native infrastructure on AWS and other major clouds.
  • Demonstrated expertise with infrastructure-as-code tooling, specifically Terraform, in large-scale environments.
  • Hands-on experience with Docker and Kubernetes, including AWS EKS operations.
  • Experience building or supporting production AI/ML platforms, including GPU infrastructure and model serving.
  • Strong DevOps practices including CI/CD, automated testing, observability, and Linux/networking fundamentals.
  • Demonstrated experience leading the use of enterprise-authorized AI-assisted software development tools.
  • Understanding of responsible AI usage in engineering workflows and coaching others on compliant patterns.
  • Proficiency in Go or Python for automation and tooling (preferred).
  • Experience with MLOps frameworks like Kubeflow or MLflow (preferred).
  • Working knowledge of ML frameworks such as PyTorch, TensorFlow, or Hugging Face (preferred).
  • Exposure to multi-cloud or hybrid cloud architectures (preferred).

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