As a Lead Software Engineer at JPMorgan Chase within the Test Integration and Implementation Payments Technology Team in the Corporate & Investment Bank line of business, you serve as a seasoned member of an agile team to support, design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible leading critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Required qualifications, capabilities, and skills
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
- Leads initiatives to improve the reliability and stability of the applications and platforms using data-driven analytics to improve service levels, proactively identifying and solving technology-related bottlenecks in areas of expertise
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- Contributes to software engineering communities of practice and events that explore new and emerging technologies
- Adds to team culture of diversity, equity, inclusion, and respect.
- 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.
- Ability to apply Agentic AI frameworks to automate and augment core Environment Management functions such as intelligent incident detection and remediation, automated root cause analysis, predictive alerting, self-healing infrastructure, runbook automation, and observability enrichment to reduce toil and accelerate MTTR.
- Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls.
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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and proficient applied experience.
- Hands-on practical experience in system design, application development, testing, and operational stability
- Proficient in coding in one or more languages (Java and/or Python)
- Demonstrated knowledge of applications or infrastructure in a large-scale technology environment both on premises and public cloud i.e. Kubernetes and Amazon Web Services
- Experience with monitoring tools like Geneos, Dynatrace, Datadog.
- Develop and maintain Splunk dashboards, reports, and alerts .
- Experience with ticketing systems, such as ServiceNow and Jira Service Desk
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Overall knowledge of the Software Development Life Cycle
- Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
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Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Preferred qualifications, capabilities, and skills
- xperience building reliability automation for large-scale integration and test environments.
- Experience implementing automated remediation, self-healing patterns, or runbook automation.
- Experience designing governance for AI-assisted engineering usage, including traceability and audit requirements.
- Experience building observability enrichment and alert quality improvements to reduce noise and accelerate recovery.
- Experience mentoring engineers and leading technical initiatives across multiple teams.