Lead Software Engineer - AI and Automation

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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