About The Role

Join one of the world's most innovative financial institutions, where your engineering skills will help protect and power technology at a global scale. At JPMorganChase, we invest in our people, our platforms, and our future — offering you the opportunity to grow alongside cutting-edge technology and a team that values collaboration, curiosity, and continuous improvement.

As a Sr Lead Security Engineer at JPMorganChase within the Cybersecurity & Technology Controls team in Corporate Technology, you will design and deliver scalable, resilient full-stack solutions that sit at the intersection of security and engineering excellence. You will play a key role in advancing our digital asset custody platform, driving AI-assisted engineering practices, and ensuring our systems are built to the highest standards of reliability and security. This role offers the opportunity to work across a broad technology landscape — from cloud infrastructure to blockchain integrations — while contributing to a team that is shaping the future of secure financial technology.

Job responsibilities

Design and develop scalable, resilient systems using Java or Python, contributing to continual, iterative improvements for product teams through hands-on software solutions, development, and technical troubleshooting
Develop and operate microservices including API design, service decomposition, and reliability patterns such as timeouts, retries, idempotency, and dead-letter queues
Build and maintain integrations using AWS services including API Gateway, SQS, SNS, S3, and RDS; troubleshoot production issues across services and infrastructure
Produce clear design and architecture artifacts — including service boundaries, data flows, sequence diagrams, and architecture decision records — ensuring implementation aligns with design constraints
Gather, analyze, and synthesize large, diverse data sets to develop visualizations and reporting that drive continuous improvement of software applications and systems; identify hidden problems and patterns in data to improve coding hygiene and system architecture
Design and implement components of a digital asset custody platform, including signing pipeline services, policy evaluation engines, and chain adapter integrations across EVM and non-EVM networks
Architect, deploy, and manage scalable applications using AWS cloud services across the full software development lifecycle
Drive team adoption of enterprise-authorized AI-assisted engineering practices — including AI-assisted code review, refactoring, test strategy acceleration, and incident root-cause analysis — while establishing consistent validation standards for secure coding, peer review, and automated testing
Apply knowledge of Software Development Life Cycle toolchain capabilities, including enterprise-authorized AI-assisted development and automation tools, to improve the value realized through automation and delivery speed
Contribute to software engineering communities of practice and events that explore new and emerging technologies

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability in a large corporate environment using one or more modern programming languages and database querying languages
Overall knowledge of the Software Development Life Cycle and agile methodologies including CI/CD, application resiliency, and security
Knowledge of software applications and technical processes within a technical discipline such as cloud, artificial intelligence, machine learning, or mobile
Experience with digital asset or financial services platforms, including familiarity with transaction lifecycle, key management concepts, or custody workflows
Experience architecting and building with AWS cloud services
Demonstrated experience leading effective use of approved AI-assisted software development tools — including coding, code review, test acceleration, and 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 and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Preferred qualifications, capabilities, and skills

Familiarity with modern front-end technologies
Experience with blockchain technologies, including public chains such as Ethereum or other EVM-compatible networks
Experience with advanced CI/CD pipeline design and optimization
Experience with Terraform scripting and infrastructure-as-code best practices

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