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 Sr. Lead Software Engineer at JPMorgan Chase within the Enterprise Technology – Infrastructure Platforms Group, 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
- Lead the development, implementation, and delivery of software-defined networking (SDN) solutions for software-defined datacenters, ensuring timelines, technical requirements, and network performance standards are met across highly resilient and scalable infrastructure
- Design, develop, and implement network automation solutions and infrastructure management platforms using Python, Go, JavaScript, and modern web technologies to reduce manual intervention and improve operational efficiency
- Architect and deploy SDN solutions and network orchestration systems across multi-cloud environments (AWS, Azure, GCP, Private cloud), integrating monitoring frameworks with existing tools to ensure infrastructure reliability
- Develop and maintain network automation frameworks, APIs, and tooling that integrate with network devices, controllers, and monitoring systems, writing clean, maintainable infrastructure-as-code that adheres to coding standards
- 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
- Drive decisions that influence the product design, application functionality, and technical operations and processes
- Collaborate with network architects, security teams, product owners, and infrastructure engineers in an Agile scrum environment, conducting code reviews and troubleshooting network applications to meet performance and reliability requirements
- Mentor junior engineers on network automation and infrastructure-as-code principles while influencing technical direction by evaluating emerging SDN technologies, automation using AI tools, and DevOps methodologies
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts, and 5+ years applied experience
- In particular, 5+ years of experience in network infrastructure development and DevOps networking engineering
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Strong experience developing software with Python, Go, JavaScript, RUST and Shell Scripting for network automation and infrastructure management, with experience leading development projects in an Agile environment with cross-functional teams
- Deep understanding of networking protocols and technologies including BGP, VRF, MPLS, IP, TCP, and UDP as applied to datacenter contexts, with hands-on experience in cloud networking architectures such as VPC, CNI, hybrid cloud connectivity patterns, and engineering experience with VMware NSX-T for software-defined networking solutions including NSX-T logical switching, routing, load balancing, distributed firewall, micro-segmentation, overlay and underlay network configuration
- Experience with CI/CD tools (specifically Jenkins) for infrastructure automation pipelines, container orchestration using Kubernetes and containerized network functions, network automation frameworks and infrastructure-as-code practices (Ansible, Terraform)
- 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.
- Exceptional problem-solving and critical thinking skills with ability to decompose complex network infrastructure challenges, perform root cause analysis across distributed systems, debug and optimize both network infrastructure components and automation software, and apply systems thinking to anticipate cascading impacts of changes.
- Excellent communication and collaboration skills to work effectively with cross-functional teams including physical network operations, security, and application platform teams, with demonstrated ability to learn quickly, adapt to changing technologies, and make sound technical decisions under pressure
- Ability to tackle design and functionality problems independently with little to no oversight
Preferred qualifications, capabilities, and skills
- Experience with network visibility and analytics tools and network virtualization platforms including Cisco ACI, Calico, OVN (Open Virtual Network), and Cilium for container networking
- Knowledge in policy-as-code frameworks , network security automation practices, and zero-trust architecture implementation
- Industry-recognized networking certifications such as AWS Certified Advanced Networking - Specialty, Azure Network Engineer Associate, or Google Cloud Professional Cloud Network Engineer.
- Demonstrated ability to leverage AI-powered development tools and Specification-Driven Development (SDD) methodologies using platforms such as Claude Code, GitHub Copilot, or similar tools to enhance network automation development velocity and code quality