Lead Software Engineer - AI/ML, Selenium, Java

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer- Quality Assurance at JPMorganChase within the Commercial & Investment Bank Technology, your goal is to ensure that our applications work as they should and meet customers’ needs. In this role, you will operate as a Delivery Manager / Test Architect, accountable for test strategy, delivery governance, automation architecture, and stakeholder alignment across squads and releases. You will drive continuous improvement through modern engineering practices, including AI-assisted approaches to requirements understanding, test optimization, execution analytics, and defect triage—ensuring outcomes remain traceable, controlled, and aligned to quality standards.

Own and drive end-to-end QA delivery for assigned programs/releases, including scope, plans, milestones, dependencies, RAID management, and executive-level status reporting and Build and mentor high-performing QA teams through coaching, goal setting, and capability development (automation, architecture, domain knowledge, modern QA practices). 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. Define and maintain the test strategy and test architecture (functional, regression, integration, API, UI, performance, data validation), ensuring alignment to business risk, regulatory needs, and SDLC controls. Establish and govern quality engineering standards across teams (test design patterns, automation coding standards, framework usage, branching strategy, test data strategy, environment strategy). Ensure documentation and auditability of QA processes and outcomes within ALM/QTEST/JIRA, aligned to internal quality and delivery controls. Lead automation roadmap and architecture decisions, including framework evolution, tooling selection, reusability patterns, and CI/CD integration (e.g., Jenkins) to improve speed-to-signal and reliability and provide technical leadership to engineers: guide design reviews, ensure maintainability, reduce flakiness, and drive best practices such as test pyramids, service virtualization/mocking, and contract testing where applicable. Implement and promote AI-assisted quality engineering practices (with appropriate controls), such as: NLP/LLM-supported requirements analysis to identify gaps/ambiguities and improve acceptance criteria readiness AI-assisted test planning and coverage optimization (human-reviewed and traceable to requirements) Intelligent failure analytics (clustering, anomaly detection) to speed defect triage and stabilize pipelines Own the quality metrics framework (defect leakage, automation coverage, execution health, flakiness rate, cycle time, escape rate) and drive actionable improvements with engineering and product partners and Drive test data and environment governance, including data provisioning approaches, masking principles, data quality checks, and environment stability initiatives. Govern defect management and triage: ensure severity/priority standards, root cause analysis, preventive actions, and timely communication across stakeholders and Partner with Product Owners, Engineering Managers, Architects, and Operations to ensure release readiness, sign-offs, and controlled deployments with clear rollback/contingency planning. 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. Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to