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Among top performers, you can make a direct and meaningful impact.\nAs a Senior Lead Infrastructure Engineer at JPMorgan Chase within the Corporate Sector - Employee Platforms team, you exhibit both depth and breadth of knowledge across software, applications, and technical processes spanning multiple technical disciplines. You specialize in a specific domain within infrastructure engineering to promote programs and initiatives that integrate multiple technologies and applications. As part of the Branch Technology engineering team, you build software-defined platforms that manage and automate technology across the Retail Branch network. You operate in a small, focused team where engineers work across the stack, own delivery end to end, and work close to the architecture while maintaining a high bar and moving quickly. As an AI-Native Systems Engineer, you are a core member promoting the transition from a traditional C#, .NET, and PowerShell estate to an agentic-first platform. 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You engage closely with emerging agentic capabilities on the CDAO Fusion Studio platform and operate with the autonomy required in a lean, high-impact team.\nJob responsibilities\nApplies 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\nUses 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.\nWorks with other platforms to architect and implement changes required to resolve issues and modernize the organization and technology processes\nDrives results and implements multiple complex programs\nDrives thought leadership within the product line\nResponsible for infrastructure engineering in accordance with business requirements and executes work according to compliance standards, risk and security, and business objectives\nLeads 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.\nRequired qualifications, capabilities, and skills\nFormal training or certification on infrastructure engineering concepts and 5+ years applied experience \n7+ years of experience working in software engineering with strong hands-on delivery\nDemonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.\nAbility to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.\nDeep knowledge of cloud infrastructure and multiple cloud technologies (ability to operate in and migrate across public and private clouds)\nAn AI-native way of working, using AI across the development lifecycle to build faster and broader than a single specialization allows\nDemonstrable experience building agentic or LLM-integrated systems in production\nWorking knowledge of multi-agent frameworks such as Google ADK, LangGraph, or AutoGen\nExperience with MCP tool integration or tool-calling API pattern\nProficiency in Python, with familiarity across C#, .NET, or PowerShell\nFluency with AI-assisted development tools such as VS Code, GitHub Copilot, or Claude Code\nPreferred qualifications, capabilities, and skills\nExperience with CDAO Fusion Studio or Fusion 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