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AI Senior Lead Infrastructure Engineer

JPMorgan Chase
Columbus, USA
Full-time
Lead · 5+ years experience
Onsite
Discovered 3 weeks ago

Full Job Posting

  • Applies deep technical expertise and problem-solving methodologies focused on analyzing complex data and systems, anticipating issues, considering upstream and downstream implications, and advising on mitigation actions
  • 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.
  • Works with other platforms to architect and implement changes required to resolve issues and modernize the organization and technology processes
  • Drives results and implements multiple complex programs
  • Drives thought leadership within the product line
  • Responsible for infrastructure engineering in accordance with business requirements and executes work according to compliance standards, risk and security, and business objectives
  • 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
  • Architect, build, and maintain agentic AI solutions that automate manual or repetitive infrastructure management tasks, ensuring solutions are secure, auditable, and production-grade
  • Contribute to tooling strategy and vendor/product evaluations by prototyping, performing technical due diligence, and recommending fit-for-purpose approaches for Mainframe/Midrange automation
  • Partner with operations, SRE, architecture, risk, and control functions to ensure AI capabilities are implemented safely, transparently, and in alignment with JPMorgan Chase policies and regulatory expectations
  • Mentor engineers through pairing, code/design reviews, and technical coaching; help build sustainable technical leadership depth within the organization
  • 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.
  • Adept in specific infrastructure technology and programming languages
  • Deep knowledge of cloud infrastructure and multiple cloud technologies (ability to operate in and migrate across public and private clouds)
  • 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
  • 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

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