Lead Software Engineer - Reliability & Support

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.

• 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.

• 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 Service Now 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.)

  Preferred qualifications, capabilities, and skills

• experience 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.