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Software Engineer III - Data - Agentic Commerce

JPMorgan Chase
London, GBR
Full-time
Mid
Onsite
Discovered 2 weeks ago
awsdatabricksdelta-lakeicebergjavakubernetes

Full Job Posting

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
  • Practical cloud native experience (AWS preferred), including containers and Kubernetes

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