Software Engineer III- AI, LLM & Data Platforms

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the Consumer & Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities: Deliver end-to-end platform features for agent-based artificial intelligence use cases, from technical design through production deployment, monitoring, and ongoing improvements Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team. 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. Build and maintain reusable services and components that enable teams to create, evaluate, and operate LLM-powered agents at scale Implement evaluation and testing approaches for LLM systems, including quality measurement, regression testing, and error analysis to improve reliability over time Develop observability capabilities such as tracing, metrics, logs, and analytics to support healthy operations and fast troubleshooting Apply security, safety, and governance-by-design patterns, including guardrails, access controls, and audit-ready operational practices aligned to security and governance requirements Collaborate with product managers and stakeholders to define success metrics, prioritize work, and deliver measurable business impact Partner across engineering, data, security, and risk and compliance stakeholders to ensure solutions are secure, stable, and scalable Produce clear documentation and reference implementations that accelerate adoption and promote responsible, consistent engineering practices Required qualifications, capabilities and skills: Formal training or certification on software engineering concepts and 3+ years applied experience Proficiency in Python with strong software engineering fundamentals, including testing practices, version control, and code review Experience building and operating production services, including incident readiness, performance tuning, and operational stability for data-intensive systems Hands-on experience delivering machine learning and/or LLM-enabled features into production environments, including monitoring and post-deployment iteration Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices Practical experience with prompt engineering and retrieval-augmented generation (RAG), including evaluation methods and quality measurement Ability to design maintainable systems and make sound technical decisions through ambiguity, balancing delivery speed, risk, and long-term sustainability Strong communication skills, including the ability to explain technical trade-offs to both technical and non-technical stakeholders Strong collaboration skills and demonstrated ability to partner effectively across teams to deliver outcomes Preferred qualifications, capabilities and skills: Experience with agent orchestration frameworks (for example, LangGraph, LlamaIndex, or comparable orchestration approaches) and LLM evaluation tooling Experience with continuous integration/continuous delivery (CI/CD) practices and production deployments using Docker and Kubernetes Familiarity with vector databases, embedding pipelines, and related approaches used in retrieval-augmented generation solutions Experience with cloud and machine learning platforms such as Amazon Web Services or Databricks (or comparable platforms) Experience contributing to governance, validation approaches, or guardrail frameworks for enterprise-scale artificial intelligence solutions