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 - AI and Automation at JPMorgan Chase within the Corporate Technology - Infrastructure Management AI and Automation Solutions Engineering team, you are an integral part of an agile software engineering team that works to design, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Develops secure high-quality production code, and reviews and debugs code written by others
- 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.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Owns the full software lifecycle for multiple automation/AI initiatives: design, implementation, testing, deployment, and production support
- Develops services and workflows that integrate with infrastructure platforms and operational tooling (APIs, events, job runners, control planes)
- Engineers agentic AI solutions that safely automate tasks (tool use, workflow execution, validation, guardrails, fallbacks)
- Defines and implement observability (metrics, logs, traces), SLOs, alerting, and incident playbook
- Acts as a technical advisor across the team: architecture reviews, design decisions, and tradeoff discussions
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s): Python, JavaScript/Typescript, Go, or SQL
- 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
- Strong proficiency in Python and experience building services and automation frameworks; working knowledge of JavaScript for tooling/UI/integrations
- Strong SQL skills and experience designing data models and working with relational databases
- Hands-on experience deploying and operating workloads on Kubernetes (deployments, services, scaling, configuration, troubleshooting)
- Demonstrated technical leadership as a senior IC (leading designs, driving delivery, and influencing across teams).
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
- SRE and/or DevOps experience, including production operations, incident response, and reliability engineering practices
- Experience building automation for infrastructure/operations (orchestration, remediation, self-service, runbooks-as-code)
- Experience with agentic AI frameworks and model-to-tool integration patterns (prompt/tool design, structured outputs, evaluation/monitoring)
- Familiarity with MCP and building secure tool servers/connectors; strong understanding of access controls and auditability in tool execution
- Experience with event-driven architectures and workflow engines
- Prior experience in regulated environments with strong risk and control expectations