Lead Infrastructure Engineer – Infrastructure (HPE NonStop/Tandem) with AI/Automation
As the Lead Infrastructure Engineer at JPMorganChase within the IP organization, 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.
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.
- Collaborate with cross-functional teams to plan and execute system upgrades and installations.
- Document configurations, procedures, and troubleshooting steps for knowledge sharing and compliance.
AI / Automation add-ons
- 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).
- Design and maintain automation workflows (runbooks/self-healing) that use predictive signals and operational telemetry to automate common remediation steps safely with approvals and guardrails.
- 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.
- Use AI tooling to accelerate root-cause analysis, change-impact assessment, and post-incident reviews while ensuring human validation for production actions.
Required qualifications, capabilities and skills
- 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.
- Excellent analytical and problem-solving skills.
- Strong documentation and communication abilities.
- Ability to work independently and as part of a team in a fast-paced environment.
AI / Data / Automation skills
- 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.
- Familiarity with prompting and evaluation practices (structured prompts, reproducibility, grounding to runbooks, measuring usefulness/accuracy, and reducing hallucinations through verification).
- Working knowledge of automation/scripting used to integrate AI insights into operations (e.g., runbook automation, ticket enrichment, automated diagnostics), with strong change-control discipline.
Preferred qualifications
- 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.