As a Senior Lead Infrastructure Engineer - Capacity & Scalability Management at JPMorganChase within the Consumer and Community Banking team, you exhibit both depth and breadth of knowledge regarding software, applications, and technical processes across multiple technical disciplines. You also have a specialization in a specific domain within infrastructure engineering to drive programs or initiatives consisting of multiple technologies and applications.
Job responsibilities
- Uses enterprise-authorized AI capabilities within the work environment to accelerate analysis of complex infrastructure signals and documentation of mitigation options, validating outputs and handling operational data according to sensitivity and security requirements.
- Leads reuse-first adoption of AI-assisted practices across delivery and automation routines to reduce recurring issues, ensuring changes are validated, traceable and auditable, and aligned to resiliency and security expectations.
- Own the end-to-end Capacity Management strategy and operating model for critical CCB applications (standards, KPIs, governance, continuous improvement).
- Lead cross-functional capacity planning and risk management across Application, Infrastructure, and SRE; proactively identify risks and drive remediation to closure; Set and enforce capacity governance across platforms and applications; chair/co-lead working groups to align priorities and remove blockers.
- Modernize capacity processes, data flows, and tooling by architecting and delivering scalable, resilient solutions; influence platform roadmaps as needed.
- Advance observability and analytics through consolidated dashboards, real-time KPIs, forecasting, peak calendars/event flags, and SLA-backed alerting.
- Drive automation via governed runbooks (approvals/rollback) for actions such as headroom uplift, throttling non-critical load, and pool rebalancing.
- Build and operationalize AI skills/agents to streamline recurring workflows (risk detection, RFIs/evidence, remediation tracking) with controls, auditability, and measurable outcomes.
- Use enterprise-authorized AI to accelerate monitoring, analysis, and documentation, validating outputs and handling data per sensitivity/security requirements.
- Apply reuse-first, AI-assisted approaches to identify recurring risks and improve remediation workflows, ensuring resiliency/security-aligned validation.
- Produce executive-ready reporting and communicate portfolio status, risks, and recommendations; translate technical findings into business impact; Lead multiple parallel workstreams with clear milestones and accountability, while mentoring engineers and championing cost/resilience/performance trade-offs.
- Formal training or certification on infrastructure engineering concepts and 5+ years applied experience
- Knowledge of one or more areas of infrastructure engineering such as: hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience or performance assessments; 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.
- Proven expertise in managing or governing technology capacity (or adjacent ITIL processes) at enterprise scale; Strong understanding of infrastructure KPIs (CPU, memory, I/O, network), application performance, and monitoring/observability tooling.
- Hands-on experience building and leveraging dashboards and analytics (e.g., Grafana, Dynatrace, ELK) and BI platforms (Power BI, Qlik, Tableau) for multi-source analysis.
- Proficiency with one or more scripting/programming languages (e.g., Python, Go, JavaScript) to automate data pipelines, reporting, and runbooks.
- Hands-on experience designing and building AI skills/agentic workflows (e.g., integrating LLM-based automation into operational processes), including governance, controls, and production rollout.
- Hands-on cloud experience (e.g., AWS) with the ability to design, develop, and deploy secure, scalable infrastructure; familiarity with multi-region patterns and SRE practices.
- Exceptional communication and stakeholder management; able to influence decisions, build partnerships, and drive change across diverse teams.
- Track record of improving IT processes to maximize efficiency and user experience; strong documentation discipline and governance mindset; Demonstrated experience using enterprise-authorized AI capabilities within the work environment, with strong validation habits and awareness of data sensitivity.
- Ability to validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
- Modern emerging technologies