Designs and develops secure, scalable agentic commerce solutions for payments. Responsibilities include building agent orchestration components and MCP servers, developing Python services, creating Databricks data pipelines, supporting MLOps workflows, evaluating agent behavior, implementing observability, and improving operational stability. The role requires cloud-native engineering, API and event-driven services, LLM application development, responsible AI practices, Kubernetes, and secure production software delivery.
Are you ready to push the boundaries of what’s possible in payments technology? At JPMorganChase, you’ll join a collaborative team where your ideas drive real impact. You’ll have the opportunity to grow your career, work with cutting-edge AI and machine learning, and help deliver trusted, market-leading products. We value diversity, inclusion, and continuous learning—your contributions matter here.
As a Software Engineer III in Payments Technology within the Commercial and Investment Bank, you will design, build, and deliver secure, scalable agentic commerce solutions. You’ll work on B2B agent platforms that negotiate, onboard, and communicate with corporate suppliers, as well as the data and machine learning pipelines that power these agents. You will contribute to NEO, our governed agent platform, and help shape the future of payments technology. You’ll be part of an agile team that values innovation, collaboration, and operational excellence.
Job Responsibilities:
- Execute software solutions, design, development, and technical troubleshooting for agent components, including orchestration logic, agent tools, and task queues for autonomous workflows
- Build and maintain MCP (Model Context Protocol) servers that provide agents secure access to CRM, supplier directory, and payments data sources
- Develop secure, high-quality production code in Python, and review and debug code written by others
- Build data pipelines and feature engineering jobs on Databricks to prepare payments data for model training and agent retrieval
- Contribute to the MLOps path for optimization and prediction models, including training jobs, automated tests, and promotion of model artefacts from development through UAT to production
- Write evaluations for agent behaviour (offline test sets, regression suites in CI/CD, LLM-as-judge checks) and instrument services with OpenTelemetry tracing
- Apply enterprise-authorized AI-assisted development tools to improve code quality and delivery speed, validating AI outputs for correctness, performance, and security
- Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability of agents and model services
- Foster a team culture of diversity, opportunity, inclusion, and respect
Required Qualifications, Capabilities, and Skills:
- Formal training or certification in software engineering concepts and applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Proficiency in Python and coding in one or more additional languages (e.g., Java, TypeScript, SQL)
- Experience building and consuming APIs and event-driven services in a cloud environment
- Experience with data processing frameworks such as Apache Spark and working with large structured datasets
- Working knowledge of LLM-based application development, including prompting, tool calling, retrieval, and evaluation
- Experience using approved AI-assisted software development tools, with sound judgement on validating outputs
- Understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs and outputs
- Solid understanding of agile methodologies such as CI/CD, application resiliency, and security
- Practical cloud native experience (AWS preferred), including containers and Kubernetes
Preferred Qualifications, Capabilities, and Skills:
- Experience with agent frameworks (e.g., Google ADK, LangGraph) and agent protocols such as MCP, A2A, or AG-UI
- Experience with Databricks, MLflow, and Delta Lake or Apache Iceberg
- Exposure to model serving on Kubernetes and to model monitoring (drift, latency, accuracy)
- Familiarity with CRM platforms such as Salesforce and their APIs
- Exposure to payments, commercial card, or supplier onboarding (KYC) processes
- Familiarity with Terraform and infrastructure as code
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
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