Agentic

JPMorgan Chase & Co.•Columbus, OH

About The Position

This role involves leading the end-to-end design and delivery of scalable Java services and APIs, optimizing for performance, resiliency, and maintainability. The engineer will architect and implement cloud-native solutions on Amazon Web Services, applying well-structured patterns for reliability, observability, and cost-aware scalability. A key aspect of the role is mentoring engineers through code reviews, design guidance, and establishing pragmatic engineering standards. Collaboration with product, design, and data partners is essential to define technical strategies that improve user experience and automate key workflows. The position also includes building and optimizing data pipelines using Databricks and Apache Spark, driving engineering excellence in CI/CD, automated testing, and secure software development. A significant focus will be on driving the adoption and governance of AI-assisted engineering practices to enhance code quality, delivery speed, and operational outcomes, while establishing validation standards and promoting reuse of patterns within the SDLC/TLM toolchain. The engineer will apply knowledge of tools within the Software Development Life Cycle toolchain, including AI-assisted development and automation capabilities, to improve value realized by automation at scale.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Advanced Java experience, including building Spring Boot–based services in a microservices architecture
Experience with Java (Core & EE, Spring Boot, Spring MVC, Spring Cloud)
Practical experience delivering system design, application development, automated testing, and operational stability for production systems
Hands-on experience building cloud-native applications on Amazon Web Services (e.g., compute, storage, database, container, and serverless services)
Experience with continuous integration and delivery and modern build/version control practices (e.g., Git, Maven/Gradle, and pipeline automation)
Proficiency with automated testing approaches and frameworks (e.g., JUnit and mocking frameworks) and a strong quality-first mindset
Experience with relational databases and SQL, including data modeling and performance considerations for high-throughput systems
Knowledge of messaging and integration patterns, including event streaming technologies such as Kafka
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.

Nice To Haves

Advanced Python experience for automation, data engineering, or machine learning enablement
Experience building and deploying agentic or AI-assisted workflows, including evaluation and human-in-the-loop validation patterns
Experience with infrastructure as code and cloud provisioning automation (e.g., Terraform, CloudFormation, or AWS CDK)
Experience with containerization and orchestration (e.g., Docker and Kubernetes) for scalable service operations
Experience with CockroachDB and Go (Golang) in distributed system environments

Responsibilities

Lead end-to-end design and delivery of scalable Java services and APIs, optimizing for performance, resiliency, and maintainability in production environments
Architect and implement cloud-native solutions on Amazon Web Services, applying well-structured patterns for reliability, observability, and cost-aware scalability
Mentor engineers through thoughtful code reviews, design guidance, and pragmatic engineering standards that raise quality and accelerate delivery
Collaborate with product, design, and data partners to define technical strategies that improve user experience and automate key workflows
Build and optimize data pipelines using Databricks and Apache Spark to enable analytics and machine learning workflows at scale
Drive engineering excellence across continuous integration and delivery, automated testing, and secure software development practices
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.