Senior Lead Software Engineer, AI Platforms

Be an integral part of an agile engineering team that is constantly pushing the boundaries of what is possible across cloud, infrastructure, and AI/ML platforms.

As aSenior Lead Software Engineer at JPMorganChase within the Corporate Sector, Infrastructure Platforms team, you will play a critical role in designing, building, and operating secure, scalable, and resilient infrastructure platforms that power enterprise AI/ML workloads.

You will help deliver market-leading technology products in a secure, stable, and highly available manner while driving meaningful business impact through deep technical expertise, engineering leadership, and strong problem-solving capabilities.

In this role, you will partner closely with AI/ML engineering teams, platform teams, product owners, and infrastructure stakeholders to translate complex compute, storage, networking, GPU, and scalability requirements into production-ready platforms formulti-GPU and multi-node model training.

You will also help advance automation, developer productivity, responsible AI-assisted engineering practices, and operational excellence across the software delivery lifecycle.

Job Responsibilities


* Design, develop, test, and deliver secure, high-quality production code; review, debug, and improve code written by others.


* Architect, build, and operate secure, scalable cloud infrastructure platforms optimized for multi-GPU and multi-node AI/ML training workloads.


* Partner with AI/ML, data science, and platform engineering teams to translate compute, storage, networking, GPU, and scalability needs into robust infrastructure requirements and platform capabilities.


* Drive technical design decisions that influence product architecture, application functionality, infrastructure strategy, and operational effectiveness.


* Monitor, manage, and optimize cloud and GPU infrastructure resources for performance, reliability, utilization, scalability, and cost efficiency.


* Build and maintain CI/CD pipelines, automation frameworks, and infrastructure-as-code solutions to streamline ML platform deployment, operations, and lifecycle management.


* Provide technical leadership and guidance to engineers, contractors, and vendor partners, ensuring solutions align with business priorities, engineering standards, security expectations, and long-term platform strategy.


* Apply deep knowledge of the Software Development Life Cycle toolchain, including enterprise-approved AI-assisted development and automation capabilities, to improve engineering productivity and automation at scale.


* Champion firmwide SDLC frameworks, engineering standards, secure coding practices, resiliency expectations, and operational best practices.


* Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readine...