Principal Software Engineer - Developer Platform Engineering

If you are looking for a game-changing career at one of the world’s leading financial institutions, you’ve come to the right place.

As a Principal Engineer, Developer Platform Engineering at JPMorganChase within the Commercial and investment Bank Digital Channels and Connectivity team, you will own the technical strategy and execution for developer platform capabilities across a portfolio of 1,000+ services and a 1,000-engineer organization. You will establish whether the portfolio can handle 10x load and build continuous, automated ways to answer that question reliably at any time. You will influence standards, release readiness, and architectural decisions as a peer to senior engineering leaders, while enabling application teams to adopt performance-first, resilient engineering practices through great tooling and clear guardrails.

Job responsibilities

• Define and own a portfolio-wide capacity evaluation methodology to answer “can we handle 10x?” through workload modeling, bottleneck identification, and automated assessment at scale
• Build a developer platform engineering practice by delivering paved roads, self-service tooling, and templated performance test harnesses that teams can adopt with minimal specialist support
• Set technical direction for performance, capacity, and resiliency standards, including service level objectives (SLOs), error budgets, and performance budgets, and drive adoption through scorecards and release-readiness gates
• Design and implement automated performance evaluation pipelines (load, stress, capacity, soak) embedded in continuous integration and continuous delivery (CI/CD), with regression detection and actionable reporting
• Establish front-end performance standards for backend-for-frontend and React applications, including Core Web Vitals, bundle budgets, and latency decomposition across request flows
• Lead resiliency and chaos engineering strategy to validate autoscaling behavior, dependency failure handling, and graceful degradation patterns under extreme load
• Build observability-driven capacity intelligence by correlating performance test signals with production telemetry, real user monitoring, and synthetic monitoring to surface portfolio-level risk and per-service hotspots
• Coach and enable performance engineers and application teams on performance-first design, shifting testing and validation earlier through self-service and inner-sourced frameworks
• Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
• 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 at scale.
• Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data. Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse

Required qualifications, capabilities and skills

• 15+ years of overall engineering experience, with 8+ years in performance engineering, capacity planning, or developer platform engineering for high-traffic distributed systems
• Demonstrated experience scaling engineering practices across large organizations (500+ engineers) through self-service platforms, maturity models, and adoption playbooks
• Formal training or certification on software engineering concepts and 10+ years applied experience
• Hands-on software engineering proficiency in Java and Spring Boot, with strong familiarity in modern front-end ecosystems such as React and server-side rendering
• Deep expertise in workload modeling and capacity estimation, including statistical analysis of latency and throughput and translating portfolio goals into measurable service-level outcomes
• Strong application performance monitoring and observability experience (for example, Dynatrace or OpenTelemetry), including distributed tracing across request chains and continuous profiling approaches
• Experience embedding performance automation into CI/CD pipelines with regression detection, environment-aware test execution, and enforceable quality gates
• Proficiency with load testing tools (for example, JMeter, k6, or Gatling) and service virtualization or fault injection approaches for dependency isolation
• Kubernetes and cloud platform experience (for example, Amazon Elastic Kubernetes Service on Amazon Web Services), including autoscaling strategies, infrastructure as code, and cost-per-transaction awareness
• Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data. Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse

Preferred qualifications, capabilities and skills

• Experience with front-end performance optimization, including Core Web Vitals, Lighthouse CI, bundle analysis, and browser-based synthetic testing (for example, Playwright or WebPageTest)
• Experience with data platform and payment system performance, such as database tuning, event streaming throughput optimization, batch processing profiling, and rate-limit validation
• Strong systems and cloud performance background, including Linux tooling, Java Virtual Machine tuning, containers, networking, and modern optimization
• Familiarity with service mesh technologies and traffic-control patterns such as backpressure, circuit breaking, and cost-efficiency analysis
• Practical application of large language models and agent-based automation for workload generation, anomaly detection, capacity report automation, or test maintenance at scale

This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.