Software Engineer III - Data - Agentic Commerce

In this role you will design, build, and operate secure, scalable agent-based commerce solutions within JPMorganChase’s Payments Technology group. You’ll contribute to B2B agent platforms and the data and ML pipelines that power them, including governance via NEO. You’ll work in an agile team focused on innovation, collaboration, and operational excellence, shaping the future of payments technology. This is a hands-on, impact-driven role at a leading financial services institution.

• Design, develop, and troubleshoot agent components (orchestration logic, agent tools, task queues) for autonomous workflows
• Build MCP (Model Context Protocol) servers to secure access to CRM, supplier directory, and payments data sources
• Write secure, production-grade Python code and review others’ code
• Create data pipelines and feature engineering jobs on Databricks for model training and agent retrieval
• Contribute to MLOps initiatives (training jobs, tests, deployment between development, UAT, production)
• Write evaluations for agent behavior and instrument services with OpenTelemetry tracing
• Apply AI-assisted development tools responsibly, validating outputs for correctness, performance, and security
• Identify opportunities to automate recurring issues to improve agent and model service stability
• Foster a diverse, inclusive, and respectful team culture

• Formal training or certification in software engineering concepts and applied experience
• Hands-on system design, application development, testing, and operational stability
• Proficiency in Python and at least one other language (Java, TypeScript, SQL)
• Experience building/consuming APIs and event-driven services in a cloud environment
• Experience with data processing frameworks (Apache Spark) and large structured datasets
• Working knowledge of LLM-based application development (prompting, tool calling, retrieval, evaluation)
• Experience using approved AI-assisted software development tools with validation of outputs
• Understanding of responsible AI use in engineering workflows (data sensitivity, secure handling)
• Solid understanding of agile methods including CI/CD, resiliency, and security
• Practical cloud-native experience (AWS preferred), including containers and Kubernetes

• collaboration
• respect for diversity and inclusion
• curiosity and continuous learning
• Python
• Java
• TypeScript