This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions. As a Manager of Software Engineering at JPMorganChase within the Asset and Wealth Management organization, you lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team’s work adheres to compliance standards, business requirements, and tactical best practices. Job responsibilities • Build holistic QE strategies around multi-year, multi application programs • Design, develop, and maintain reusable automated frameworks, tooling and utilities . Build CI automation capability • Design, develop, and maintain automated performance testing frameworks and utilities. Implement and manage CI/CD pipelines for performance test orchestration and automated result generation. • Execute load, stress, and scalability tests using tools such as JMeter, LoadRunner, Gatling, BlazeMeter, and similar. • Integrate and leverage monitoring tools (APM, AppDynamics) and logging platforms (ELK Stack, OpenSearch, Splunk, Grafana) for real-time analysis and reporting. • Collaborate with cross-functional teams to define performance requirements and ensure robust coverage.Analyze test results, identify bottlenecks, and recommend optimizations. Support pre- and post-deployment performance validation in AWS environments. • People management for around 6-8 technofunctional QEs • Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams. • 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 and support capacity unlock initiatives. Required qualifications, capabilities, and skills • Formal training or certification on software engineering* concepts and 5+ years applied experience. In addition, demonstrated coaching and mentoring experience • Strong understanding of the AWM functioning and trading • Strong hands-on experience with AWS stack (EC2, S3, Lambda, CloudWatch, etc. • Proficiency in performance testing tools: JMeter, LoadRunner, Gatling, BlazeMeter etc. Experience with CI/CD tools (Jenkins, GitLab CI, etc.) and test orchestration. • Exposure to APM tools (AppDynamics, Dynatrace) and logging/monitoring platforms (ELK Stack, OpenSearch, Splunk, Grafana). • Solid programming/scripting skills (Python, Java, Groovy, NodeJS, SQL). • Excellent analytical, communication, and leadership abilities • Strong programming skills in Python, NodeJS, Java, Groovy, SQL, big data and an appetite to be able work on diverse technology at scale • Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data. • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.