Lead Software Engineer - Java/Python, React

Join a dynamic team driving innovation in AI-first engineering. Unlock your potential and shape the future of application integration and streaming.
As a Lead Software Engineer at JPMorgan Chase within the Horizontal Engineering team, you will deliver AI-first horizontal engineering capabilities for the AIS product line. You will design, build, and ship reusable components, automation, and cross-product accelerators to improve operational stability and engineering consistency. You will leverage AI agents across multiple engineering workflows, always maintaining accountability for quality and outcomes. You will play a key role in shaping technical solutions and driving standardization across AIS products.
Job responsibilities
Design and deliver secure, high-quality production code for reusable AIS-wide capabilities, including shared components, automation, APIs, SDKs, infrastructure-as-code, and cross-product engineering patterns.
Execute creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems.
Lead AI-first engineering delivery by using AI agents across analysis, code generation, testing, documentation, controls evidence, configuration management, reusable component development, and operational diagnostics.
Establish disciplined human-plus-AI workflows, including context curation, prompt and skills management, review checkpoints, approval gates, escalation paths, auditability, and output validation.
Drive standardization across AIS products by reducing duplicated engineering work, creating reusable libraries and modules, improving observability, and automating recurring operational or engineering issues.
Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Provide senior engineering judgment through design challenge, code review, debugging, quality oversight, mentoring, and coaching of engineers working in an AI-enabled delivery model.
Partner with AIS product teams, architecture, and engineering stakeholders to shape and deliver a governed backlog of cross-product work aligned to the AIS strategic Book of Work.
Lead communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies, AI-first delivery, and engineering standardization.
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
Advanced proficiency in Java/Python, with strong experience across system design, application development, testing, deployment, automation, CI/CD, infrastructure-as-code, and modern SDLC 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, platform engineering, or controls engineering).
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
Experience with React for building modern, responsive front-end applications
Practical cloud-native experience.
Preferred qualifications, capabilities and skills
Experience with cross-product engineering, horizontal capabilities, reusable platforms, shared services, or engineering standardization across multiple product lines.
Experience with data lakes, chaos engineering, cloud native development or compliance engineering in regulated financial services.
Experience building AI agents, agent-assisted engineering workflows, automated testing, documentation generation, anomaly detection, or developer productivity tooling.
Experience with multi-cloud or cloud-portable platform patterns across private cloud, AWS, Azure, or GCP.