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# Lead Software Engineer - Data - Agentic Commerce

- **Company:** JPMorgan Chase & Co.
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Java (Programming Language), Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Apache HTTP Server, Automation of Tests, Software Quality, Continuous Integration, Software Debugging, Payment Systems, Python (Programming Language), Machine Learning, Language Modeling, Software Engineering, Software Systems, TypeScript, Apache Spark, Generative AI, AI Coding Agents, Agentic-AI, Data Lakes, Kubernetes, Information Technology, Production Code, Performance Monitor, Machine Learning Operations, Databricks, Agent2Agent Protocol
- **Published:** October 2, 2026
- **Apply:** https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/requisitions/preview/210795906

## About the Role

* Formal training or certification in software engineering concepts
 * Hands-on experience delivering system design, application development, testing, and operational stability
 * Advanced proficiency in Python and proficiency in one or more additional languages (e.g., Java, TypeScript)
 * Experience shipping LLM-based applications or agents to production, including tool calling, retrieval, and evaluation
 * Experience productionizing ML models, including training pipelines, model registries, CI/CD for models, and serving behind APIs
 * Demonstrated experience leading effective use of approved AI-assisted software development tools
 * Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling
 * Proficiency 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 IT systems
 * Practical cloud native experience
 * Proficiency with Kubernetes and Amazon EKS, micro-VM isolation, and sidecar patterns, with hands-on experience applying security at multiple layers

Preferred Qualifications, Capabilities, and Skills:

 * Experience with agent frameworks and protocols (Google ADK, LangGraph, MCP, A2A, AG-UI)
 * Experience with Databricks, MLflow, Spark, and Delta Lake or Apache Iceberg
 * Experience serving models on Kubernetes, including GPU workloads and fine-tuned small language models
 * Experience with optimization or pricing models and exposing them as services
 * Experience with fine-grained authorization and OpenTelemetry

## Description

As a Lead Software Engineer in Payments Technology within the Commercial and Investment Bank, you will play a pivotal role in designing, developing, and delivering trusted technology products. You will own major components of our B2B agentic commerce agents end to end, from multi-agent negotiation and onboarding to production ML model workflows. You will help drive secure, stable, and scalable solutions, foster a collaborative team culture, and contribute to the advancement of AI-powered payments. Your expertise will directly impact our technology and the clients we serve., * Execute creative software solutions, design, development, and technical troubleshooting

 * Own the design and delivery of production agents, including orchestration, negotiation, supplier onboarding, and outreach
 * Build production paths for optimization and prediction models, including training, automated testing, and serving on Kubernetes
 * Design agent workflows that ensure pricing, eligibility, and policy decisions remain in deterministic services
 * Develop secure, high-quality production code and review and debug code written by others
 * Establish evaluation and observability for agents and models, including regression suites, scoring, traces, and performance monitoring
 * Prepare agents and models for model risk review, producing documentation, test evidence, and controls
 * Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed
 * Apply knowledge of tools within the Software Development Life Cycle toolchain to enhance automation
 * Identify opportunities to eliminate or automate remediation of recurring issues
 * Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs and technical credentials


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