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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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As an AI Senior Lead Infrastructure Engineer within the Infrastructure Platform Data & Specialty Services team, you will apply deep technical expertise to analyze complex data and systems while driving multi-technology programs. You will architect, build, and maintain agentic AI solutions to automate repetitive infrastructure management tasks, ensuring all outputs are secure, auditable, and production-grade. Your daily work involves leveraging enterprise-authorized AI capabilities to accelerate analysis of infrastructure signals, performing technical due diligence on vendor products, and mentoring engineers through code reviews and technical coaching. You will utilize cloud infrastructure across public and private environments, focusing on Mainframe and Midrange automation. Key skills include proficiency in distributed systems, API design, CI/CD, and observability tools. You must navigate complex risk and control considerations to ensure all infrastructure engineering aligns with strict security, resiliency, and compliance standards.
Analyze complex data and systems to anticipate issues and advise on mitigation actions.
Architect and implement infrastructure changes to modernize organizational processes and technology.
Integrate enterprise-authorized AI capabilities into delivery and automation routines to reduce recurring issues.
Build and maintain agentic AI solutions to automate manual or repetitive infrastructure management tasks.
Evaluate vendor products and prototype tools for Mainframe/Midrange automation.
Ensure all engineering work complies with risk, security, and regulatory standards.
Mentor engineers through technical coaching, code reviews, and design feedback.
What we're looking for
Formal training or certification on infrastructure engineering concepts and 5+ years of applied experience.
Knowledge of infrastructure engineering areas such as hardware, networking, databases, storage, deployment, integration, automation, scaling, resilience, or performance.
Experience using enterprise-authorized AI capabilities to support infrastructure workflows with strong validation habits and data sensitivity awareness.
Ability to review and validate AI-assisted recommendations while ensuring outcomes align with resiliency, security, and auditability expectations.
Proficiency in specific infrastructure technologies and programming languages.
Deep knowledge of cloud infrastructure and multiple cloud technologies across public and private clouds.
Experience designing production solutions with strong non-functional requirements like scalability, performance, and security.
Working knowledge of risk and control considerations in enterprise technology, including SDLC, change management, and auditability.
Strong engineering fundamentals including API design, distributed systems, data modeling, testing strategy, CI/CD, and observability.
Proven ability to lead technical delivery in a matrixed organization and mentor other engineers through code and design reviews.
Knowledge of Mainframe and Midrange automation, operational tooling, and platform management practices (preferred).
Exposure to agentic AI patterns and controls in production contexts (preferred).
Track record partnering with SRE and operations teams on reliability engineering and toil reduction (preferred).
Experience building integrations across heterogeneous infrastructure ecosystems (preferred).
Experience in financial services or similarly regulated industries with understanding of governance and audit expectations (preferred).
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