AI Enablement

JPMorgan Chase & Co.•Jersey City, NJ

1d•$133,000 - $185,000

About The Position

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III- Python / AWS / AI Enablement at JPMorganChase within the Asset & Wealth Management Technology team, you will design and build scalable services and platforms that power investment and wealth solutions. You will work hands-on across application code and cloud infrastructure, delivering reliable, secure, and well-observed systems. You’ll collaborate closely with product, engineering, data/AI partners, and control functions to enable modern AI capabilities (including MCP and AI tooling integrations) while maintaining high standards for engineering excellence and operational stability.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience
Python development experience (building production services/applications).
Hands-on experience with AWS or another major cloud platform (designing, deploying, and operating cloud workloads).
Working knowledge of MCP (Model Context Protocol) and experience using AI tools to build or integrate AI-enabled features/workflows.
Strong software engineering fundamentals: data structures, APIs, testing, reliability, performance tuning, and code quality practices.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Experience with CI/CD pipelines and modern delivery practices (e.g., automated testing, artifact/version management, release strategies).
Strong communication skills and ability to partner effectively across engineering, product, and control stakeholders.

Nice To Haves

Java coding experience (e.g., maintaining or integrating with JVM-based services).
Exposure to front-end development and React (e.g., building small UI components, supporting full-stack delivery).
Experience with containers and orchestration (e.g., Docker, Kubernetes/EKS) and/or serverless patterns.
Familiarity with observability tooling and practices (metrics, distributed tracing, SLOs/SLIs).

Responsibilities

Design, develop, test, and deploy Python-based microservices, APIs, and data-driven applications supporting AWM business workflows and platforms.
Build and operate cloud-native solutions on AWS (or equivalent cloud platforms), focusing on scalability, resiliency, performance, and cost awareness.
Implement MCP (Model Context Protocol) and integrate AI tools to enable safe, consistent, and reusable model/application interactions and developer workflows.
Develop and maintain infrastructure and automation using infrastructure-as-code and CI/CD practices.
Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
Apply 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.
Collaborate with cross-functional teams to define technical requirements, design patterns, and service interfaces; participate in architecture and code reviews.
Establish strong observability practices (logging, metrics, tracing), drive incident response readiness, and continuously improve operational excellence.
Contribute to secure engineering practices (authentication/authorization patterns, secrets management, dependency hygiene, and SDLC controls).
Mentor junior engineers and contribute to a culture of inclusion, ownership, and continuous improvement.