Director of Software Engineering and ML - Agentic Commerce - Executive Director

London Source: WhatJobs

See advert for pay

Overview

In this role, you lead a London team of software, data, and ML engineers to deliver AI-enabled B2B agents and productionize their ML models on the firm’s governed platform. You own delivery, people, and technical direction, ensuring safe, compliant production of AI agents for corporate clients. You collaborate with cross-functional partners to scale from first release to enterprise-wide usage, shaping architecture and governance. This is a chance to drive impactful multi-agent systems in a prestigious global bank and advance AI-enabled commerce.

Responsibilities

Build and lead the London engineering team across levels, including hiring and career development
Own the delivery roadmap for B2B agentic commerce agents from first release through scale-out
Set multi-agent architecture direction including pricing policy control, evaluation, and observability
Establish ML production operating model: training, testing, promotion, serving, monitoring, retraining
Lead model risk, cybersecurity, and control reviews for production agents and models
Partner with NEO platform, data platform, client channel, and business teams; contribute reusable components
Promote enterprise-approved AI-assisted engineering practices, validation standards, and pattern reuse
Manage resources, budget, vendor selection, and tactical operations; influence cross-team decisions
Lead evaluations with external vendors and internal teams on architectural designs and credentials
Communicate progress, risks, and trade-offs to senior technology and business leadership
Foster a culture of diversity, inclusion, and respect

Key requirements

Formal training or certification in software engineering concepts with leadership experience
Hands-on experience delivering system design, development, testing, and operational stability
Track record of building and scaling engineering teams and developing leaders
Experience delivering production AI/ML systems, including LLM-based applications or agents
Strong understanding of the ML lifecycle (data pipelines, training, deployment, monitoring, governance)
Experience using AI-assisted software development tools with validation for correctness, performance, and security
Strong understanding of responsible AI, data sensitivity, secure handling of inputs/outputs, and security resiliency
Proficiency across SDLC and agile practices including CI/CD, resiliency, and security
Practical cloud-native experience
Proficiency with Kubernetes and Amazon EKS, micro-VM isolation, sidecar patterns, and security design across stack
Ability to influence and communicate with executive audiences