Senior Lead Software Engineer - Terraform, AWS

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 Senior Lead Software Engineer at JPMorganChase within the Technology organization, 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

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• Develops secure and high-quality production code, and reviews and debugs code written by others
• Drives decisions that influence the product design, application functionality, and technical operations and processes
• Serves as a function-wide subject matter expert in one or more areas of focus
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
• Influences peers and project decision-makers to consider the use and application of leading-edge technologies
• Adds to the team culture of diversity, opportunity, inclusion, and respect
• Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
• 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.
• Applies 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.

Required qualifications, capabilities, and skills

• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Advanced in one or more programming language(s)
• Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• Ability to tackle design and functionality problems independently with little to no oversight
• Practical cloud native experience
• Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field
• Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.
• 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.