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Lead Software Engineer - Python
lead · Technology / Software Development
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorgan Chase in the Athena Core DevTools team, you will design and build systems that accelerate and improve the daily work of thousands of engineers. You will shape the engineering experience from code creation to production release, partnering with teams across technology, platform engineering, and governance. You’ll work on high-impact projects that enhance developer productivity, quality, and controls. Our team values collaboration, innovation, and a focus on delivering tools that make a real difference. You will be part of a culture that encourages ownership and continuous improvement.
Job Responsibilities:
- Build and evolve developer-facing products, including IDE experiences, web tooling, test infrastructure, and SDLC workflows
- Improve productivity and confidence for thousands of engineers through impactful tooling
- Own features throughout their lifecycle: discovery, design, implementation, rollout, telemetry, and operational support
- Translate ambiguous challenges into scalable platform capabilities
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Partner with controls and audit stakeholders to implement effective, low-friction engineering controls
- Influence engineering standards and best practices across a broad developer community
- Design and implement IDE and editor capabilities for smarter navigation and code intelligence
- Drive AI adoption in local development tools to enhance software development practices
- Develop static analysis and auto-remediation tools to prevent errors before production
- Build test frameworks and scheduling systems for large-scale workloads, including cloud-based execution
- Create platform tooling to identify code duplication, dead code, and opportunities for codebase simplification
Required Qualifications, Capabilities, and Skills:
- Strong software engineering fundamentals and passion for developer tooling
- Proficiency in multiple programming languages, with emphasis on Python; familiarity with TypeScript/React and SQL for full stack development
- Solid understanding of testing, reliability, and maintainable system design
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Ability to operate independently in ambiguous problem spaces and collaborate effectively across teams
- Skill in turning loosely defined requirements into robust, widely adopted solutions
- Working knowledge of modern engineering workflows, including testing, CI/CD, static analysis, version control, and deployment
Preferred Qualifications, Capabilities, and Skills:
- Experience building developer tools or platforms at scale
- Familiarity with large-scale Python codebases
- Exposure to AI-driven development tools and practices
- Experience with cloud-based test execution and infrastructure
- Knowledge of regulatory or audit requirements in engineering environments
- Background in platform migrations or large-scale software initiatives
- Experience influencing engineering standards and best practices