AI Senior Lead Infrastructure Engineer

Job responsibilities

- 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 JPMorganChase 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.

- Mentor engineers through pairing, code/design reviews, and technical coaching; help build sustainable technical leadership depth within the organization.

- Champion JPMorganChase values, including a culture of inclusion, accountability, disciplined execution, and continuous improvement.

Required qualifications, capabilities, and skills

- 5+ 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.