AI Senior Lead Software Engineer

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorganChase within the Infrastructure Platform AI Automation Mainframe and Midrange team, you are 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.\
- Lead the design and delivery of AI-enabled capabilities for Mainframe and Midrange systems management, focusing on intelligent automation that improves stability, efficiency, and operator experience.
- Translate the Advanced Capabilities Engineering tower strategy into executable technical plans, epics, and implementation roadmaps for assigned initiatives.
- Architect, build, and maintain agentic AI solutions that automate manual or repetitive infrastructure management tasks, ensuring solutions are secure, auditable, and production-grade.
- Own end-to-end engineering delivery for features/services you lead: requirements clarification, solution design, build, testing strategy, rollout, monitoring, and operational readiness.
- Partner with operations, SRE, architecture, risk, and control functions to ensure AI capabilities are implemented safely, transparently, and in alignment with policies and regulatory expectations.
- Establish and follow strong engineering standards (design reviews, coding practices, CI/CD, documentation, observability, and incident readiness), and help uplift consistency across the team.
- Drive reliability outcomes by ensuring solutions meet resiliency, scalability, performance, and security objectives—enhancing service reliability and operational effectiveness without introducing unmanaged risk.
- Contribute to tooling strategy and vendor/product evaluations by prototyping, performing technical due diligence, and recommending fit-for-purpose approaches for Mainframe/Midrange automation.
- Provide clear technical communication to stakeholders: design rationale, tradeoffs, delivery status, risks/issues, and recommended mitigations.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
- Ability to tackle design and functionality problems independently with little to no oversight
- 10+ years of hands-on software engineering experience delivering enterprise-grade systems (design, build, test, deploy, operate).
- Strong experience building automation platforms/services and integrating with infrastructure operations workflows; experience with AI-assisted automation and/or decision-driven systems is required.
- Experience designing and delivering production solutions with strong non-functional requirements (resiliency, scalability, performance, security, and operational excellence)
- Working knowledge of risk and control considerations in enterprise technology (e.g., SDLC controls, change management, access controls, auditability, data handling)
- Proven ability to lead technical delivery in a matrixed organization: influencing partners, unblocking execution, and driving outcomes across multiple teams/functions.
- Strong engineering fundamentals: API/service design, distributed systems concepts, data modeling, testing strategy, CI/CD, and observability (logs/metrics/traces).
- Experience mentoring and guiding other engineers via code reviews, design reviews, and technical feedback.
Preferred qualifications, capabilities, and skills
- Knowledge of Mainframe and Midrange automation, operational tooling, and platform management practices
- Exposure to agentic AI patterns and controls (human-in-the-loop design, guardrails, prompt/tool governance, evaluation/monitoring) in production contexts
- Track record partnering with SRE and operations teams on reliability engineering and operational efficiency improvements (e.g., toil reduction, incident automation, runbook modernization)
- Experience building integrations across heterogeneous infrastructure ecosystems (e.g., job schedulers, monitoring/alerting, ticketing workflows, configuration systems)
- Experience in financial services or similarly regulated industries, with understanding of governance, controls, and audit expectations