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As a Lead Software Engineer at JPMorganChase within the Chief Technology Office (CTO) - Identity & Access Management (IAM), you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for building critical technology solutions across multiple technical areas working closely with various business functions and partners in support of the firm’s business objectives.

You will be responsible for building, integrating, and operating enterprise-scale automation and an agentic, human-gated testing ecosystem that accelerates delivery while protecting quality, controls, and auditability. This role will align with user-journey-led quality, partner integration readiness, and an agentic test framework. While AI-enabled IDEs can accelerate test creation, this position exists to engineer what AI alone cannot reliably own in a complex, fast-changing IAM stack: deterministic test data, stable test environments, integration simulation, coverage integrity, and release-grade evidence across environments. You will champion end-to-end journeys across AuthE Global Product stack, enabling True Continuous Delivery with fast, compliant, evidence-based releases.

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
• 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.
• Set and run journey quality strategy to convert product intent into risk-based acceptance criteria, measurable quality outcomes, and clear entry/exit standards for key IAM journeys
• Design coverages that stays current with maintaining a living journey map (personas → flows → tests) and continuously tune regression & performance testing scope based on change impact and production learnings
• Engineer partner-facing integration validation by implementing contract tests, stubs/simulators, and automated integration/acceptance suites that emulate partner behaviors and isolate dependency risk
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Improve reliability of the testing system in reducing flakiness and time-to-diagnosis through stability patterns (selectors/fixtures, determinism, parallelism strategy), and clear quarantine/triage rules
• Own defect prevention loop to lead reproduction → triage → fix validation → prevention, feeding failure patterns back into coverage, data strategies, and automation improvements
• Report “journey health” with clarity by providing executive-ready readiness reporting that explains risk, coverage, and release posture in plain language—backed by evidence, not volume metrics
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies and must add to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on software/quality engineering, software development in testing automation 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) and framework(s) (i.e., Java, BDD suites - cucumber/gherkin, SQL, Rest API, Jira, etc.)
• Proficiency hands on engineering, automation depth and continuous delivery methods (UI automation - playwright/selenium), along with experience in API testing with a track record of building stable suites (selectors/fixtures, reliability patterns, deterministic data, flake control)
• Understands enterprise testing ecosystems with familiarity and ability to standardize across frameworks such as FiTest+ and/or other harnesses used by the organization (alongside modern OSS stacks)
• Build and operationalize an agentic testing ecosystem to design, implement, and run a human-gated agent framework that accelerates test authoring, selection, execution, and triage—connecting agents to real tools (frameworks, CI/CD, APIs, data sources) while enforcing guardrails for correctness, determinism, security, and auditability
• Release readiness & governance with entry/exit criteria, evidence-based sign-off, and strong partnership in Release Management in a CD operating model - along with curate and version a trusted test catalog, continuously measuring agent “fitness” (precision, drift, false positives/negatives), and ensure outputs remain release-grade across environments
• Establish and maintain strong partnership across Product/Engineering/Operations and other lines of business, with executive-ready reporting and risk articulation
• 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

• Experience building and operating MCP Servers/clients to connect LLM agents to tools (APIs, databases, and internal services)

• Demonstrate ability to implement human-in-the-loop guardrails to test review standards, signed/versioned catalogs, drift controls, and coverage integrity mechanisms
• Demonstrate proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• Experience designing and implementing agentic/LLM-assisted pipelines for continuous test selection, execution, and reporting across environments, operating MCP Servers/clients to connect LLM agents to tools (APIs, databases, and internal services)