Software Engineer III- Python / AWS / AI Enablement

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.

Job 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.

Required Qualifications, Capabilities, and Skills

• 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.

Preferred Qualifications, Capabilities, and Skills

• 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).