Lead Software Engineer - Automation & Development at JPMorgan Chase, Jersey City, NJ
Lead Software Engineer - Automation & Development at JPMorgan Chase in Jersey City, NJ. Est. up to $200k. Apply with swipeandhired.
Join a team where your engineering decisions directly improve platform stability, delivery speed, and operational excellence. You will build automation that scales, reduces recurring issues, and helps teams ship with confidence. If you enjoy solving complex infrastructure challenges with modern engineering practices, this role offers the opportunity to make a measurable impact.
As a Lead Software Engineer at JPMorganChase within Infrastructure Platforms in the Corporate Sector , you drive the design and delivery of resilient automation that improves how virtualized and container platforms are provisioned, updated, and operated. You own end-to-end technical outcomes, partnering across teams to reduce risk, increase reliability, and accelerate delivery. You apply strong engineering fundamentals and a practical approach to AI-assisted development to solve complex problems while maintaining high standards for security, performance, and quality.
Job responsibilities
- Design and deliver software solutions that automate virtualized cluster lifecycle management from provisioning through decommissioning
- Automate configuration, patching, compliance enforcement, and operational controls through platform and infrastructure APIs
- Lead production-grade deployments by owning technical solutions, establishing runbooks, and driving operational readiness
- Diagnose complex issues using structured troubleshooting methods, root-cause analysis, and data-driven decision-making
- Identify recurring operational problems and implement durable automation to eliminate manual remediation and improve stability
- Establish effective, validated AI-assisted engineering practices to improve code quality, testing effectiveness, and incident resolution
- Define and improve engineering standards across system design, testing strategy, and service reliability for distributed systems
- Document architecture and design decisions to improve auditability, maintainability, and long-term scalability
- Collaborate with global engineering partners to align on priorities, influence technical direction, and deliver outcomes across teams
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong system design fundamentals, including API design, data modeling, testing strategy, and operational stability
- Proficiency in Go, Java, or Python to build scalable, reliable server-side services
- Experience using AI-assisted development tools responsibly, including setting team validation standards for correctness, performance, and security
- Strong understanding of data sensitivity and secure handling practices when using AI-assisted workflows and developer tooling
- Hands-on experience with virtualization and automation concepts, including virtual machine lifecycle and core resource domains (compute, memory, storage, networking)
- Experience automating Kubernetes or Red Hat OpenShift cluster lifecycle and day-two operations, including upgrades and configuration management
- Experience developing and maintaining automated workflows in complex distributed environments, with strong troubleshooting skills
- Experience with workflow orchestration at scale (any comparable orchestration platform)
Preferred qualifications, capabilities, and skills
- Experience with Temporal or similar workflow orchestration frameworks used for large-scale automation
- Familiarity with hypervisor environments such as VMware ESXi, Red Hat OpenShift Virtualization, or kernel-based virtual machine (KVM)
- Experience building cloud-native services, including containerization, continuous integration and continuous delivery, and observability practices
- Demonstrated track record mentoring engineers and raising engineering standards through coaching, reviews, and technical leadership
- Strong interest in innovation and continuous improvement, including practical applications of AI to accelerate engineering outcomes