AI Governance Engineering Lead

Your responsibilities

Build governance into the platform

• Turn the policies, standards, and risk decisions that Risk and Infosec own 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, applications, and external providers.
• Build evaluation and regression hooks into the release path, with acceptance thresholds for models, prompts, agents, and platform changes, automated wherever practical.
• Design monitoring for control failures, model drift, anomalous use, permission breaches, and high-risk actions, with clear escalation and remediation paths.
• Build the dashboards and evidence packs that service owners, Risk, Infosec, and Internal Audit use directly, so assurance does not depend on you being in the room.

Translate across domains, and work across the firm

• Work with Risk’s AI governance specialist, Infosec, Legal, Compliance, Privacy, Records Management, and TPRM to understand what each needs, and convert it into technical requirements engineers can implement.
• Write standards and control requirements that are specific enough to build from, and explain back to non-engineers how the platform behaves and why.
• Advise AI Architecture, AI Engineering, AI Platforms, and Forward Deployed Engineering on control design, and help teams classify use cases and understand which controls apply before they build.
• Support risk-based onboarding of new foundation models, AI software, and connectors with AI Platforms and AI Security without restarting the process for every low-risk update, and work alongside Percepta so controls and operating knowledge transfer into our ownership.

What to expect when you join our firm

• Hybrid working and reasonable accommodations
• Generous Holiday policies
• Excellent Health and Wellbeing benefits including corporate membership to Wellhub
• Paid volunteer time to step away from your desk and into the community
• Support to grow through professional development courses, tuition/qualification reimbursement and more
• Maternal/paternal leave benefits and family services
• All employee events including networking opportunities and social activities
• Lunch allowance for use within our subsidized onsite canteen

Must have skills

• At least six years in software, platform, or security engineering, with a track record of building and operating things that reached production. This is an engineering role — a policy, audit, or compliance background is not what we are looking for.
• Strong Python and SQL, and hands-on ability with APIs, infrastructure as code, and CI/CD. You will build the controls, not specify them for somebody else to build.
• Real depth in identity and access: authentication, authorisation, RBAC, service principals and workload identity, secrets management, entitlement models, and least privilege.
• A practical understanding of what a technical control is and how to implement one — preventive and detective controls, secure defaults, deployment gates, and the evidence a control has to produce.
• Hands-on experience with a major cloud, ideally Azure, including logging, monitoring, and data-protection primitives.
• The ability to work with stakeholders whose domain is not yours — risk, legal, privacy, compliance, audit, security — understand what they actually need rather than the words they used, and turn it into a technical design they recognise as their requirement.
• Practical knowledge of generative AI and agentic systems: foundation models, prompts, retrieval, tools, connectors, model gateways, and autonomous workflows.
• Judgement to distinguish a control objective from a preferred implementation and apply proportionate controls based on actual risk, and clear communication — you can write a technical standard an engineer can implement, and explain to a non-engineer why the platform does what it does.

Nice to have skills

• Policy-as-code tooling such as Open Policy Agent or Rego, and automated evidence or compliance-as-code pipelines.
• Entra ID, Microsoft Purview, DLP policy design, or data classification across a Microsoft 365 estate.
• Experience securing or governing agents, tool execution, MCP servers, or other machine-to-machine interfaces.
• Experience building internal developer platforms, golden-path patterns, or self-service guardrails used by other engineering teams.
• Snowflake, Microsoft Fabric / OneLake, and governed enterprise data access patterns.
• Exposure to a regulated environment, or to Internal Audit and independent control testing. Useful context, but we will build the regulatory knowledge around you.

Supervisory responsibilities

No. This is a senior individual-contributor role with authority over the design and implementation of governance controls. The role may lead cross-functional work and coach engineers, but does not line-manage.

Potential for growth

• Mentoring
• Leadership development programs
• Regular training
• Career development services
• Continuing education courses

At Janus Henderson Investors we’re committed to an inclusive and supportive environment. We believe diversity improves results and we welcome applications from candidates from all backgrounds. Don’t worry if you don’t think you tick every box, we still want to hear from you!