Senior Lead Software Engineer- Platform / Linux Engineering

Senior Lead Software Engineer- Platform / Linux Engineering

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer- Platform / Linux Engineering at JPMorganChase within the Corporate Sector- Infrastructure Platforms, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

As a Senior Platform Engineer within Electronic Trading Services Platforms, you will be a hands-on technical leader responsible for designing, building, and tuning the Linux-based compute and infrastructure platforms that power our low-latency electronic trading businesses. This is a deeply technical, engineering-first role: you will write code and automation, design new systems, architect novel deployment models, and drive latency out of the stack at the hardware, kernel, and network layers. You will also harness modern Agentic AI tooling to accelerate delivery and multiply your impact.

You will work directly with the Electronic Trading Lines of Business to align technical strategy with business goals, solve complex compute and performance-engineering challenges, and mentor partner technical teams. You will serve as an expert, hands-on advisor to peers, leadership, and stakeholders.

Job Responsibilities

• Design, build, and operate high-performance Linux platforms for low-latency electronic trading, engineering from the hardware and kernel level up
• Architect new system designs and next-generation deployment models — including immutable infrastructure, containerization, and automated provisioning pipelines
• Perform deep performance engineering and latency optimization: kernel tuning, CPU isolation/pinning, NUMA awareness, IRQ affinity, kernel-bypass networking, and precision time synchronization
• Write production-quality code and automation to build tooling, eliminate manual toil, and improve platform reliability and speed
• Leverage Agentic AI tools and workflows to accelerate engineering tasks, automate research and remediation, and deliver against goals faster
• Drive 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
• Apply 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.
• Interface with hardware and software vendors on product roadmaps and to define the next generation of Electronic Trading Platform infrastructure
• Make decisions that influence teams' resources, budget, tactical operations, and the execution of processes and procedures
• Carry governance accountability for engineering decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations
• Deliver reusable technical solutions that can be leveraged across multiple businesses and domains and mentor and influence SRE, peer engineers, and senior stakeholders across business, product, and technology teams

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Strong programming skills with the ability to build production-grade tooling and automation (e.g., Python, Go, Bash)
• Deep, hands-on Linux engineering expertise — administration, internals, performance tuning, and benchmarking, in areas such as Red Hat Enterprise Linux and proven experience engineering for low-latency, high-throughput environments (kernel tuning, CPU/NUMA optimization, precision timing)
• Hands-on experience with kernel-bypass networking, specifically Solarflare Onload and extensive experience with configuration management, in areas such as Chef or Ansible
• 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
• Solid understanding of IPv4 networking, including the OSI model, unicast/broadcast/multicast, routing, and network segregation mechanisms
• Experience designing CI/CD pipelines and infrastructure-as-code (e.g., Terraform) and practical cloud-native and container/orchestration experience (e.g., Docker, Kubernetes)
• Experience leading cross-functional teams of technologists, and hiring, developing, and recognizing talent

Preferred Qualifications, Capabilities, and Skills

• Experience with server hardware selection, BIOS/firmware tuning, and bare-metal provisioning for performance-sensitive workloads
• Familiarity with observability and telemetry stacks (e.g., Prometheus, Grafana, ELK) and understanding of standard cryptography (certificates, key pairs, secure communication, etc.)
• Experience with market-data and messaging technologies common to electronic trading (e.g., multicast market data, low-latency messaging buses)
• Experience with database management systems, preferably PostgreSQL