Lead architecture and engineering for B2B agentic commerce and machine-learning systems in global payments. Define multi-agent architectures, authorization, information barriers, MLOps pipelines, evaluation frameworks, safety guardrails, and model-risk evidence. Develop and review secure production code, establish AI-assisted engineering standards, integrate external agents, improve operational resilience, and guide technical teams and vendors. The role requires advanced Python, production LLM agent experience, secure distributed-systems expertise, and financial-services technology knowledge.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Senior Lead Software Engineer at JPMorganChase within the Commercial and Investment Bank, Payments Technology, 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 the senior technical authority for the London team, you set the architecture for our B2B agentic commerce agents and for how ML models are trained, served, and governed as tools those agents rely on. Your decisions shape how agents acting for buyers and suppliers can transact safely at the scale of a global bank.
Job responsibilities
- Sets the technical direction and reference architecture for B2B agentic commerce: multi-agent topology, agent-to-agent (A2A) communication, tool integration over MCP, and the boundary between LLM reasoning and deterministic services
- Designs information barriers and authorization for agents that act on behalf of different counterparties, using fine-grained authorization and isolated execution where tenants may be adversarial
- Defines the MLOps architecture for models used as agent tools: training on Databricks, environment promotion, model registry, serving on Kubernetes, monitoring, and retraining cadence
- Sets engineering standards for agent quality and safety, including evaluation frameworks, guardrails, observability, and the evidence required for model risk approval
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Develops secure and high-quality production code for critical paths, and reviews and debugs code written by others
- Partners with the NEO platform team to contribute reusable capabilities back to the platform, and with product, risk, and client-facing channel teams on external agent integration
- Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, 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
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Mentors lead and associate engineers, and raises the technical bar through design reviews and written architecture decisions
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s), with strong hands-on experience in Python and/or Java required
- Deep understanding of concurrency and distributed computation, with proven experience designing multithreaded and multi-process systems, worker and task-queue architectures, and distributed workloads that are correct and observable under concurrency (consistency, idempotency, backpressure, partial failure)
- Proven experience architecting and shipping production LLM agents or multi-agent systems, including tool use, memory, evaluation, and guardrails
- Proven experience designing end-to-end ML platforms or MLOps pipelines, from training through serving and monitoring
- Experience designing secure distributed systems, including identity, authorization, and service-to-service trust
- 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 and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud native experience
- Deep proficiency with Kubernetes and Amazon EKS, micro-VM isolation (e.g., Firecracker, Kata Containers, gVisor), and sidecar architectures, with proven experience designing defense in depth across the stack: network and mTLS, workload identity, fine-grained authorization, runtime isolation for untrusted or adversarial workloads, and application-level guardrails
Preferred qualifications, capabilities, and skills
- Deep experience with agent protocols and frameworks: MCP, A2A, AG-UI, Google ADK or LangGraph
- Experience with agent payment and mandate patterns (e.g., AP2), or with regulated workflows where LLM output must be constrained and auditable
- Experience with OpenFGA or OPA/Rego, service mesh (Istio/Envoy mTLS), and micro-VM isolation (Kata, gVisor, Firecracker)
- 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 a production setting
- Domain knowledge of B2B payments, commercial card, supplier enablement, or treasury
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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