at J.P. Morgan
Bulge Bracket Investment BanksPosted 14 days ago
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**Senior Lead Infrastructure Engineer - Data Center Networking** Shape the future of a global giant as our **Senior Lead Infrastructure Engineer - Data Center Networking** in Singapore. You'll leverage extensive expertise in infrastructure engineering and advanced skills in data center networking technologies to drive high-impact projects and thought leadership. Key responsibilities include identifying complex problems, leading multi-project tasks, partnering for technological advancements, and mentoring junior engineers. Must-have skills: 5+ years in infrastructure engineering, deep expertise in Cisco, Arista, Juniper, advanced routing and switching, automation frameworks, and leading stability programs. Proven experience with enterprise AI capabilities is essential. A Bachelor's in Computer Science or related field is preferred.
- Compensation
- Not specified
- City
- Singapore
- Country
- Singapore
Currency: Not specified
Full Job Description
Location: Singapore, Singapore
As a Senior Lead Infrastructure Engineer at JPMorgan Chase within the Infrastructure Platforms, Shared Network Services, you will leverage your extensive knowledge of software, applications, and technical systems and processes in infrastructure engineering, along with advanced expertise in other disciplines. You will contribute to the development of technical methods in a specialized field or area, aligning with product development methodologies. Your role involves applying in-depth expertise and problem-solving methodologies to plan, accomplish, and review tasks across multiple large-scope projects in various technical areas.
Job responsibilities
- Identifies and solves problems of high complexity associated with infrastructure engineering technology.
- Applies extensive in-depth expertise and problem-solving to accomplish, plan, and review tasks across multiple large scope projects and businesses/domains to influence, manage and implement functional technology platforms.
- Responsible for high impact projects requiring deep technical expertise and leadership.
- Partners across other platforms to architect and implement changes required to resolve issues and modernize the organization and its technological processes.
- Drives thought leadership within the product line and/or platform.
- Extensive consideration of upstream/downstream system or technical implications and advice on mitigation actions
- Leads reuse-first adoption of AI-assisted practices across delivery and automation routines to reduce recurring issues, ensuring changes are validated, traceable and auditable, and aligned to resiliency and security expectations.
- Uses enterprise-authorized AI capabilities within the work environment to accelerate analysis of complex infrastructure signals and documentation of mitigation options, validating outputs and handling operational data according to sensitivity and security requirements.
- Mentors and coachs junior engineers and technologists
- Bachelors Degree in Computer Science, Cybersecurity, Data Science, or related disciplines
- Formal training or certification on infrastructure engineering concepts and 5+ years applied experience.
- Experience in network architecture, design, and engineering within large enterprises.
- Deep technical expertise in data center networking technologies: Cisco, Arista, Juniper, Nexus, ACI, BGP, EVPN, VXLAN, etc.
- Hands-on experience with routing and switching technologies, including BGP, OSPF, IS-IS, VLANs, STP, VPC/MLAG, and stacking.
- Practical experience with Cisco and Arista platforms (e.g., Nexus Series, Catalyst).
- Strong experience with automation frameworks (e.g., Ansible, Terraform, Python, CI/CD pipelines) for network infrastructure.
- Experience with Arista CVP, AVD, Cisco NFM, Cisco Network as Code
- Demonstrated success in leading stability and resiliency programs including change management, incident response, and RCA.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
