Senior Lead Software and Data Engineer - Agentic Commerce

Join us to shape the future of agentic commerce and machine learning at a global scale. You’ll have the opportunity to set technical direction, influence engineering culture, and deliver impactful solutions that power secure transactions for buyers and suppliers worldwide. We value creativity, collaboration, and a passion for building technology that makes a difference. At JPMorganChase, your expertise will help us push the boundaries of what’s possible and foster your career growth.

As a Senior Lead Software Engineer in Payments Technology within the Commercial and Investment Bank, you will set the architecture for B2B agentic commerce agents and the ML models they rely on. You will guide the London team in building secure, scalable solutions that enable safe transactions at the scale of a global bank. Your leadership will drive technical standards, foster innovation, and ensure the delivery of high-quality, resilient technology products. You will collaborate with cross-functional teams to integrate advanced AI and automation practices, shaping the future of payments technology.

Job Responsibilities:

- Set technical direction and reference architecture for B2B agentic commerce, including multi-agent topology and agent-to-agent communication
- Design information barriers and authorization for agents acting on behalf of different counterparties, ensuring secure and isolated execution
- Define MLOps architecture for agent tools, covering training, environment promotion, model registry, serving, monitoring, and retraining cadence
- Establish engineering standards for agent quality and safety, including evaluation frameworks, guardrails, and model risk approval evidence
- Execute creative software solutions, design, development, and technical troubleshooting beyond conventional approaches
- Develop secure, high-quality production code and review/debug code written by others
- Partner with platform, product, risk, and client-facing teams to integrate external agents and contribute reusable capabilities
- Drive adoption of enterprise-authorized AI-assisted engineering practices, establishing validation standards and promoting reuse
- Apply knowledge of the Software Development Life Cycle toolchain, including AI-assisted development and automation
- Identify opportunities to eliminate or automate remediation of recurring issues for operational stability
- Lead evaluation sessions with vendors and internal teams to assess architectural designs and technical credentials

Required Qualifications, Capabilities, and Skills:

- Demonstrate formal training or certification in software engineering concepts with advanced applied experience
- Apply hands-on experience in system design, application development, testing, and operational stability
- Show advanced proficiency in one or more programming languages, with Python required
- Architect and ship production LLM agents or multi-agent systems
- Design end-to-end ML platforms or MLOps pipelines
- Exhibit expertise in secure distributed systems, including identity, authorization, and service-to-service trust
- Lead with AI-assisted software development tools and validate AI outputs
- Understand responsible AI use, data sensitivity, and secure engineering workflows
- Demonstrate proficiency in all aspects of the Software Development Life Cycle
- Apply advanced understanding of agile methodologies such as CI/CD, application resiliency, and security
- Display in-depth knowledge of financial services industry IT systems

Preferred Qualifications, Capabilities, and Skills:

- Deep experience with agent protocols and frameworks such as MCP, A2A, AG-UI, Google ADK, or LangGraph
- Experience with agent payment and mandate patterns, or regulated workflows requiring auditable LLM output
- Experience with OpenFGA or OPA/Rego, service mesh technologies, and micro-VM isolation
- Experience with Databricks, MLflow, and model serving on Kubernetes, including GPU capacity planning and small language model inference
- Experience with knowledge graphs, GraphRAG, or organizational memory for agents
- Experience with optimization, pricing, or negotiation models in production
- Domain knowledge of B2B payments, commercial card, supplier enablement, or treasury