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.