Sr Lead Infrastructure Engineer - Python / AI
Salary not listed • Plano, TX, United States • Full-time
Posted 3d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
About the job
As a Senior Lead Infrastructure Engineer at JPMorgan Chase, you will be a core member of the Employee Platforms team, contributing to the evolution of a world-renowned company. You will specialize in infrastructure engineering, promoting programs that integrate multiple technologies and applications. Working within a small, focused team, you will own delivery end-to-end and operate close to the architecture, maintaining a high bar and moving quickly. Your role as an AI-Native Systems Engineer involves promoting the transition to an agentic-first platform, designing and developing agentic workflows, and integrating AI across the full stack to accelerate the path from idea to production. You will also help the team adopt AI-native practices and patterns, engaging closely with emerging agentic capabilities.
Responsibilities
- Analyze complex data and systems, anticipate issues, and advise on mitigation actions.
- Use enterprise-authorized AI capabilities to accelerate infrastructure signal analysis and documentation.
- Architect and implement changes to resolve issues and modernize organization and technology processes.
- Drive multiple complex programs and thought leadership within the product line.
- Perform infrastructure engineering in accordance with business requirements, compliance, risk, security, and business objectives.
- Lead reuse-first adoption of AI-assisted practices for delivery and automation, ensuring changes are validated, traceable, and auditable.
Requirements
- Formal training or certification in infrastructure engineering concepts and 5+ years of applied experience.
- 7+ years of experience in software engineering with strong hands-on delivery.
- Demonstrated experience using enterprise-authorized AI capabilities for infrastructure engineering workflows, with validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted recommendations before implementation.
- Deep knowledge of cloud infrastructure and multiple cloud technologies.
- An AI-native approach, using AI across the development lifecycle.
- Demonstrable experience building agentic or LLM-integrated systems in production.
- Working knowledge of multi-agent frameworks like Google ADK, LangGraph, or AutoGen.
- Experience with MCP tool integration or tool-calling API patterns.
- 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.