AI Governance Engineering Lead

About the role

A career at Janus Henderson is more than a job – it’s about investing in a brighter future together. Our mission is to help clients define and achieve superior financial outcomes through differentiated insights, disciplined investments, and world‑class service. We pursue this by protecting and growing our core business, amplifying our strengths, and diversifying where we have the right opportunities. Our values – Clients Come First – Always, Execution Supersedes Intention, Together We Win, Diversity Improves Results, Truth Builds Trust – are at the heart of everything we do.

This is an engineering role within Janus Henderson’s firm‑wide AI transformation. You will sit in AI Technology and report to the Head of AI Technology, working closely with Risk’s AI governance specialist and Infosec to turn policy, privacy, audit, and risk requirements into technical controls. You will not need to be an AI regulation expert; instead you will translate domain needs into working platform capabilities on our Nexus agentic workspace and Accio centralized MCP server.

The role is a senior individual‑contributor position with authority over the design and implementation of governance controls. You may lead cross‑functional work and coach engineers, but you do not have line‑management responsibilities. Opportunities for growth include mentoring, leadership development programs, regular training, career development services, and continuing education courses.

Responsibilities

• Build governance into the platform – turn policies, standards, and risk decisions from Risk and Infosec into reusable controls, policy‑as‑code, deployment gates, and secure defaults.
• Create self‑service governance patterns and templates so approved teams can build safely without repeated manual approvals, and embed control checks and evidence capture into repositories, CI/CD pipelines, infrastructure‑as‑code, and deployment workflows.
• Establish cost, usage, data‑access, and model‑access boundaries that are enforced by default through the model gateway and platform services.
• Define a proportionate lifecycle for experiments, pilots, production AI products, model changes, and autonomous agents, and set the release requirements that go with each tier.
• Engineer identity, access, and permissions – design and implement identity, authentication, authorisation, and permission patterns for agents, models, tools, connectors, and service accounts, working with enterprise IAM and AI Security.
• Implement least privilege and entitlement models that hold when a request crosses several systems, including through Accio to downstream MCP servers.
• Build the approval and human‑in‑the‑loop patterns that high‑stakes actions require, while keeping low‑risk activity self‑service.
• Implement an auditable framework for agents and end‑user‑developed applications, covering named ownership, permissions, approved data, testing, change history, and retirement.
• Build the evidence, telemetry, and evaluation layer – define and build the event and evidence model needed to reconstruct prompts, model responses, tool calls, agent decisions, approvals, data access, and cost.
• Work with AI Engineering and AI Platforms to make telemetry consistent across the model gateway, agent orchestration