As a Senior Lead Software Engineer 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.
Job responsibilities
- Execute creative software solutions, design, development, and technical troubleshooting for virtualized cluster lifecycle automation.
- Automate provisioning, configuration, patching, compliance enforcement, and decommissioning using Platform APIs
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability
- Lead production-grade deployments, own technical solutions, diagnose issues using structure troubleshooting methods.
- Work with large-scale infrastructure projects
- Document design decisions to enhance auditability.
- Mentor engineers through design reviews, code reviews, and technical guidance that elevates engineering standards.
- Collaborate, influence, and partner with peers across global teams.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong fundamentals in system design: API design, data modeling, testing strategy, and operational stability.
- Proficiency in Go, Java, or Python building scalable server-side services.
- 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 virtualization automation (VM lifecycle and resource concepts: CPU, memory, storage, networking).
- Hands-on experience automating OpenShift or Kubernetes cluster lifecycle and Day-2 operations (upgrades, configuration management, policy/compliance alignment).
- Experience developing and maintaining automated workflows in complex distributed environments; strong troubleshooting skills.
- Experience with workflow orchestration at scale (Temporal strongly preferred; similar orchestration acceptable)
Preferred qualifications, capabilities, and skills
- Hands-on experience with virtualization automation (VM lifecycle and resource concepts: CPU, memory, storage, networking).
- Demonstrated track record mentoring engineers and raising engineering standards.
- Familiarity with hypervisor environments (VMware ESXi, OpenShift, KVM).
- Cloud-native experience (containers, CI/CD, observability, reliability engineering).
- Passion for innovation, AI, and continuous improvement.
