Lead Software Engineer Python, sqlplqsl, Automation

We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.

As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank youare 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. As a core technical contributor you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.

Job responsibilities

• Executes creative software solutions design development and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Lead end to end P1/P2 production incident response triage impact analysis mitigation recovery and stakeholder updates
• Drive problem management and permanent remediation: run RCA sessions define corrective/preventive actions prioritize fixes with engineering and track closure to measurable outcomes.
• Lead TOIL reduction by identifying repetitive operational work and delivering automation using Python and SQL/PLSQL (scripts tooling workflows utilities).
• 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.
• Develops secure high-quality production code and reviews and debugs code written by others
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors startups and internal teams to drive outcomes-oriented probing of architectural designs technical credentials and applicability for use within existing systems and information architecture
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies

Required qualifications capabilities and skills

• Formal training or certification on software engineering concepts and 5 years applied experience
• Hands-on practical experience delivering system design application development testing and operational stability
• Strong hands-on engineering skills in Python and database systems (SQL/PLSQL) with a track record of building automation and diagnostic tooling.
• Exposure to AWS cloud and Kafka
• Design and deliver self-service operational tooling (including UI-based run/operate screens) using AI agents/Copilot-style capabilities to improve support throughput and consistently
• 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
• Proficiency in automation and continuous delivery methods
• Proficient in all aspects of the Software Development Life Cycle
• Advanced understanding of agile methodologies such as CI/CD Application Resiliency and Security
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g. cloud artificial intelligence machine learning mobile etc.)