Java GraphQL Software Engineer II

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you.

As a Software Engineer II at JPMorganChase within Commercial & Investment Bank - Market Monitor team, you are part of an agile team that works to enhance, design, and deliver software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Job responsibilities

• Executes standard software solution design, development, and technical troubleshooting across multiple components within a product/application/system.
• Writes secure, high-quality code primarily in Java (Spring/Spring Boot) and continuously improves code quality through modern engineering practices.
• Designs, develops, and supports microservices with strong consideration for distributed systems concerns (performance, resilience, scalability, and operability).
• Builds and maintains API layers with strong GraphQL experience (schema design, resolver patterns, authorization, pagination, error handling, and performance tuning) and complementary REST experience where needed.
• Builds and operates AWS-based solutions using cloud-native patterns (identity and access management, encryption, networking, deployment patterns, monitoring, and cost/performance considerations).
• Applies modern SDLC practices including CI/CD, code quality gates, automated security testing, and production observability (logs/metrics/traces) to improve reliability and delivery speed.
• Uses enterprise-authorized AI capabilities as a core part of daily engineering, including code generation/refactoring, automated test creation, debugging assistance, documentation, and incident triage support, while rigorously validating outputs through review, automated testing, and secure coding standards.
• Engineers AI-ready services and integrations with emphasis on current best practices, including structured inputs/outputs, schema validation, deterministic post-processing, idempotency, rate limiting, and safe degradation for AI-dependent flows.
• Applies responsible and secure AI engineering: protects sensitive data, avoids leaking confidential information into prompts/logs, designs for injection resistance, and follows governance expectations for AI usage in code and runtime workflows.
• Contributes to AI quality and reliability practices such as automated evaluations/regression testing for AI behaviors, prompt/version management where applicable, telemetry for AI interactions, and measurable quality gates to prevent AI-driven regressions.
• Writes and maintains robust automated tests using current testing technologies (e.g., JUnit 5, Mockito, Testcontainers, contract testing, API mocking, and CI-integrated test automation) to ensure coverage, reliability, and rapid feedback.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 2+ years applied experience.
• Hands-on practical experience in system design, application development, testing, and operational stability.
• Experience developing, debugging, and maintaining code in a large corporate environment with modern programming languages and database querying languages.
• Strong hands-on Java experience with Spring/Spring Boot in production environments.
• Strong GraphQL experience (schema-first design, resolver patterns, security/authorization, performance considerations).
• Strong AWS cloud knowledge designing, deploying, and supporting production systems (e.g., ECS/EKS or Kubernetes patterns, Lambda, API Gateway/ALB, S3, RDS/Aurora/DynamoDB, IAM/KMS, CloudWatch).
• Demonstrable, hands-on experience with AI-assisted software engineering using enterprise-authorized tools, including the ability to validate and harden AI-generated code/tests, and to apply AI effectively in debugging and delivery workflows.
• Working knowledge of AI engineering best practices relevant to product development (structured outputs, evaluation/testing approaches for AI behavior, basic model/tool integration concepts, and secure prompt/input handling).
• Demonstrable experience with modern automated testing practices and tooling (unit, integration, contract, and end-to-end testing; CI automation; test reliability and maintainability).
• Experience across the SDLC, including agile delivery, CI/CD, application resiliency, and security.

Preferred qualifications, capabilities, and skills

• Experience with Infrastructure as Code (IaC) using Terraform (or equivalent).
• Experience with observability and production diagnostics (e.g., OpenTelemetry, distributed tracing, actionable alerting).
• Familiarity with AI integration patterns such as tool-calling/agentic workflows, retrieval patterns, structured outputs, and automated evaluation/regression approaches (as applicable).
• Familiarity with full-stack application development.
• Strong communication skills and the ability to work effectively in a team environment.