About The Role

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 a Senior 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 for multi-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 readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

Required Skills, Capabilities, and Skills

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on experience building highly scalable and highly available infrastructure for machine learning training and/or inference workloads.
Solid system-level understanding of GPU infrastructure, accelerators, high-speed interconnects, distributed compute, and related platform technologies.
Strong experience with Kubernetes and containerization technologies, including Docker, cluster operations, workload scheduling, observability, and production troubleshooting.
Proficiency in at least one modern programming language, such as Python, Go, Java, or C#.
Demonstrated ability to independently solve complex design, scalability, reliability, performance, and functionality challenges with minimal oversight.
Deep understanding of cloud component architecture, including microservices, compute, storage, networking, security, routing, and switching technologies.
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools for coding, code review, test acceleration, troubleshooting, and engineering productivity.
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Design, develop, test, and deliver secure, high-quality production code; review, debug, and improve code written by others.

Preferred Qualifications, Capabilities, and Skills

Hands-on experience with performance monitoring, production debugging, profiling, sampling, bottleneck analysis, and capacity optimization.
Foundational understanding of NVIDIA GPU infrastructure software and ecosystem tooling, such as DCGM, BCM, NVIDIA drivers, CUDA, and training libraries.
Experience with MLOps platforms and tooling, including MLflow or similar model lifecycle management solutions.
Background in high-performance computing, distributed systems, and ML frameworks, including distributed training, Ray.io, Slurm, or similar workload orchestration technologies.
Strong knowledge of network architecture, including high-throughput and low-latency networking patterns for distributed compute and AI/ML workloads.
Familiarity with cloud data services, big data processing platforms, Linux systems, and storage technologies used in large-scale data and ML environments.
Experience designing platforms that support model training, experiment tracking, feature pipelines, model serving, and scalable inference in enterprise environments.