Lead Software Engineer-Full Stack/Multi-Cloud Security

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Lead Software Engineer, Full Stack/Multi-Cloud Security at JPMorgan Chase within the Corporate Sector- Cloud Foundational Services team, 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

• Design and deliver multi-cloud security and continuous compliance solutions across Azure, AWS, and GCP.
• Translate regulatory and policy requirements into technical controls, measurable KPIs/KRIs, and actionable engineering roadmaps.
• Build control automation for preventive, detective, and corrective controls, including validation, evidence capture, exception handling, and remediation.
• Implement audit-ready observability and traceability across services and delivery pipelines, including logs, metrics, lineage, and reporting.
• Write high-quality, maintainable production code in Python or Go, and contribute to Java components where integration or platform requirements apply.
• Engineer workflow orchestration for control execution, exception processing, and remediation across cloud and platform systems.
• Build and optimize CI/CD pipelines using Jenkins, including Dev Sec Ops quality and security gates.
• Integrate enterprise systems and APIs including Jira, Confluence, Bitbucket, identity/authentication platforms, and cloud provider services.
• Lead reliability and security engineering excellence: drive code/architecture reviews and standards; troubleshoot complex incidents with root-cause analysis; produce and maintain architecture documentation, ADRs, operational runbooks, control implementation standards; evaluate and integrate third-party tools; and lead responsible adoption of enterprise-approved AI-assisted engineering with robust validation for correctness, security, performance, resiliency, and data handling.
• 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

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Proven experience across end-to-end backend delivery: system design, development, testing, deployment, and production operations.
• Strong proficiency in Python or Go for building services and automation.
• Hands-on CI/CD expertise using Jenkins in enterprise delivery environments.
• Solid SDLC execution, including secure coding, test strategy, release governance, and operational support.
• Cloud-native engineering experience on Azure, AWS, or GCP.
• Experience implementing security, audit, and compliance controls in regulated environments.
• Production experience with Kubernetes and workflow orchestration.
• Ability to lead cross-functional delivery and mentor developers, with a strong focus on secure and resilient engineering practices.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices

Preferred qualifications, capabilities, and skills

• Experience delivering and operating security/compliance solutions and familiarity with cloud-native security services (identity, key management, logging, monitoring, policy enforcement) across Azure, AWS, and GCP.
• Experience with policy-as-code and infrastructure-as-code (for example Terraform and cloud policy frameworks).
• Experience building event-driven architectures and telemetry pipelines for control evidence and compliance reporting.
• Experience integrating third-party security/compliance products into enterprise platforms.
• Knowledge of financial-services regulatory and technology risk frameworks.
• Experience applying AI-assisted tooling to security engineering and continuous compliance workflows.
• Experience with API integration patterns and enterprise authentication/authorization mechanisms.