As a Director of Software Engineering at JPMorganChase within the Commercial and Investment Bank, Payments Technology, you lead a London team of software, data, and ML engineers that builds B2B agentic commerce agents and productionizes the ML models those agents use as tools. You own delivery, people, and technical direction for the team, and you are accountable for putting AI agents that act for corporate clients into production safely, on the firm's governed NEO agent platform.

Job responsibilities

• Builds and leads the London engineering team across associate, lead, and senior lead levels, with direct responsibility for hiring, coaching, career development, and performance
• Owns the delivery roadmap for B2B agentic commerce agents, from first production release through scale-out, in partnership with product owners, program managers, and design
• Sets technical direction with senior engineers for multi-agent architecture, deterministic control of pricing and policy decisions, evaluation, and observability
• Establishes the operating model for ML in production: how models are trained, tested, promoted across environments, served as tools, monitored, and retrained
• Leads the team through the firm's model risk, cybersecurity, and control reviews required before agents and models go live, and owns ongoing production stability
• Partners with the NEO platform team, data platform teams, client channel teams, and business stakeholders, and contributes reusable components back to the platform
• Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards and promoting reuse of effective patterns across the team
• Makes decisions that influence team resources, budget, vendor selection, and tactical operations, and is accountable for those outcomes
• 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
• Communicates progress, risks, and trade-offs clearly to senior technology and business leadership
• Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and advanced applied experience, including significant experience leading and developing engineering teams
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Track record of building and scaling engineering teams, including hiring senior engineers and developing leaders
• Experience leading delivery of production AI/ML systems, ideally including LLM-based applications or agents
• Strong understanding of the ML lifecycle in production: data pipelines, training, deployment, monitoring, and governance
• Demonstrated experience leading effective use of approved AI-assisted software development tools, with the ability to set organizational expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use, including data sensitivity considerations, secure handling of inputs and outputs, and adherence to resiliency and security expectations
• Proficient in all aspects of the Software Development Life Cycle and an advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• Practical cloud native experience
• Proficiency with Kubernetes and Amazon EKS, micro-VM isolation, and sidecar patterns, with the depth to direct security design across multiple layers of the stack, from network and workload identity through runtime isolation to the application
• Ability to influence across organizational boundaries and communicate with executive audiences

Preferred qualifications, capabilities, and skills

• Experience with agent frameworks and protocols (MCP, A2A, Google ADK, LangGraph) and agent evaluation practices
• Experience with Databricks, MLflow, and model serving on Kubernetes in AWS
• Experience delivering AI systems through model risk management or equivalent regulatory review
• Experience running a team within a federated platform model, contributing to and consuming shared platform capabilities
• Domain knowledge of B2B payments, commercial card, accounts payable, supplier enablement, or KYC onboarding
• Experience building a new engineering hub or team