Sr Lead Infrastructure Engineer - Python / AI

Become a member of a team where you can contribute significantly to shaping the future of a world-renowned and influential company. Among top performers, you can make a direct and meaningful impact. As 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. You design and develop agentic workflows, MCP tool integrations, and automation that define the next generation of branch technology, collaborating with experienced engineers under a clear architectural direction. You remain hands-on with the most complex problems and play an influential role in shaping the platform’s evolution. You work AI-native by default, leveraging AI across the full stack to accelerate the path from idea to production. In addition to building agentic systems, you help the team adopt them by establishing practices and patterns that make AI-native development the standard. 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. Job responsibilities 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. Required qualifications, capabilities, and skills Formal training or certification on infrastructure engineering concepts and 5+ years applied experience 7+ years of experience working in software engineering with strong hands-on delivery 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. Deep knowledge of cloud infrastructure and multiple cloud technologies (ability to operate in and migrate across public and private clouds) An AI-native way of working, using AI across the development lifecycle to build faster and broader than a single specialization allows Demonstrable experience building agentic or LLM-integrated systems in production Working knowledge of multi-agent frameworks such as Google ADK, LangGraph, or AutoGen Experience with MCP tool integration or tool-calling API pattern Proficiency in Python , with familiarity across C#, .NET, or PowerShell Fluency with AI-assisted development tools such as VS Code, GitHub Copilot, or Claude Code Preferred qualifications, capabilities, and skills Experience with CDAO Fusion Studio or Fusion SmartSDK