JPMorgan Chase
From JPMorgan Chase's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
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
Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
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.
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