Lead Infrastructure Engineer - Infrastructure (HPE NonStop/Tandem) with AI/Automation
Summary
Lead a team maintaining HPE NonStop payment infrastructure, integrating AI/automation for monitoring, troubleshooting, and secure key management to ensure high availability and compliance.
Assume a vital position as a key member of a high-performing team that delivers infrastructure and performance excellence. Your role will be instrumental in shaping the future at one of the world's largest and most influential companies.
As a Lead Infrastructure Engineer at JPMorgan Chase within the Corporate Technology team, you apply deep knowledge of software, applications, and technical processes within the infrastructure engineering discipline. You will be responsible for designing, deploying, and supporting payment infrastructure deployments. The ideal candidate will have hands-on experience with HPE NonStop hardware and architecture, as well as a strong understanding of related subsystems and secure key management. This role is responsible for configuring, maintaining, and troubleshooting HPE NonStop systems and associated components, ensuring high availability and security for enterprise operations. Continue to evolve your technical and cross-functional knowledge outside of your aligned domain of expertise.
Job responsibilities
- Configure, maintain, and troubleshoot HPE NonStop hardware and architecture.
- Manage and configure Enterprise Secure Key Managers to ensure robust security for sensitive data.
- Set up and maintain Etinet servers, ensuring optimal performance and integration with HPE NonStop systems.
- Understand and support HPE NonStop subsystems (Mediacom, TMF, KMSF), and their relationship to hardware for effective troubleshooting and planning of upgrades or installations.
- Utilize and manage SCF, ZZSTO, ZZZCIP, and ZZKRN utilities for system configuration, monitoring, and maintenance.
- Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements.
- Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations.
- Leverage AI-assisted operations (AIOps) techniques to improve incident triage, reduce MTTR, and proactively detect infrastructure risks (e.g., anomaly detection on system/EMS logs, event correlation, early-warning indicators).
- Build and curate high-quality operational knowledge (KB articles, runbooks, known-error records) that can be used by AI assistants to provide accurate, auditable troubleshooting guidance.
- Partner with SRE/Observability and Cyber teams to evaluate, implement, and govern AI-enabled monitoring and alerting, ensuring model outputs are explainable, traceable, and compliant with security controls.
- Document configurations, procedures, and troubleshooting steps for knowledge sharing and compliance, collaborate with cross-functional teams to plan and execute system upgrades and installations.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- In-depth knowledge of HPE NonStop hardware and architecture, including system configuration and maintenance.
- Experience with Enterprise Secure Key Managers: ability to configure and manage secure key solutions in an enterprise environment.
- Proficiency in configuring Etinet servers and integrating them with HPE NonStop systems.
- Strong understanding of HPE NonStop subsystems (Mediacom, TMF, KMSF) and their interaction with hardware, especially for troubleshooting and upgrade/install planning.
- Hands-on experience with SCF, ZZSTO, ZZZCIP, and ZZKRN for system configuration and management.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
- Experience applying AIOps / ML-driven monitoring concepts (anomaly detection, alert correlation, noise reduction, trend forecasting, predictive capacity/health signals) in a production infrastructure environment.
- Ability to work with telemetry pipelines (logs/metrics/traces), define data quality expectations, and operationalize signals for automation and reliability outcomes.
- Practical experience using AI assistants for troubleshooting and documentation, with an emphasis on validation, secure handling of sensitive data, and producing audit-ready outputs.
Preferred qualifications, capabilities, and skills
- Experience implementing or operating AIOps platforms and integrating them with incident/ticketing workflows.
- Exposure to LLM governance concepts in enterprise settings (data classification, access controls, audit logging, model risk considerations).
- Experience building operational analytics (e.g., Python/SQL) to mine EMS/application logs for recurring patterns, failure modes, and leading indicators.
- Familiarity with reliability practices (SLOs/SLIs, error budgets, blameless postmortems) and using AI to improve these processes.