• 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

• Bachelors Degree in Computer Science, Cybersecurity, Data Science, or related disciplines
• 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