AI Governance Engineering Lead

Janus Henderson Investors•Denver, CO

16h•$150,000 - $190,000•Hybrid

About The Position

This is an engineering role within AI Technology, reporting to the Head of AI Technology. The role is part of a firm-wide AI transformation focused on integrating governance by design into the platform. The successful candidate will translate requirements from risk, legal, privacy, and audit specialists into technical solutions for AI applications and data platforms (Nexus and Accio). Key responsibilities include implementing identity and permissions, policy-as-code, deployment gates, and telemetry for AI workloads. The role requires strong translation skills to bridge the gap between non-technical stakeholders and technical implementation, with a focus on identity, cloud, and controls, while adapting to the rapidly evolving AI landscape.

Requirements

At least six years in software, platform, or security engineering, with a track record of building and operating production systems.
Strong Python and SQL skills.
Hands-on ability with APIs, infrastructure as code, and CI/CD.
Real depth in identity and access: authentication, authorization, RBAC, service principals and workload identity, secrets management, entitlement models, and least privilege.
A practical understanding of technical controls (preventive and detective), secure defaults, deployment gates, and required evidence.
Hands-on experience with a major cloud, ideally Azure, including logging, monitoring, and data-protection primitives.
Ability to work with stakeholders from risk, legal, privacy, compliance, audit, and security to understand needs and translate them into technical designs.
Practical knowledge of generative AI and agentic systems: foundation models, prompts, retrieval, tools, connectors, model gateways, and autonomous workflows.
Judgement to distinguish control objectives from preferred implementations and apply proportionate controls based on risk.
Clear communication skills to write technical standards and explain platform behavior to non-engineers.

Nice To Haves

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.
Snowflake, Microsoft Fabric / OneLake, and governed enterprise data access patterns.
Exposure to a regulated environment, or to Internal Audit and independent control testing.

Responsibilities

Turn policies, standards, and risk decisions into reusable controls, policy-as-code, deployment gates, and secure defaults.
Create self-service governance patterns and templates for teams to build safely, embedding control checks and evidence capture into CI/CD pipelines, infrastructure-as-code, and deployment workflows.
Establish cost, usage, data-access, and model-access boundaries enforced by default through the model gateway and platform services.
Define a proportionate lifecycle for AI experiments, pilots, production AI products, model changes, and autonomous agents, setting release requirements for each tier.
Design and implement identity, authentication, authorization, and permission patterns for agents, models, tools, connectors, and service accounts.
Implement least privilege and entitlement models that hold across multiple systems, including through Accio to downstream MCP servers.
Build approval and human-in-the-loop patterns for high-stakes actions while keeping low-risk activity self-service.
Implement an auditable framework for agents and end-user-developed applications, covering ownership, permissions, data, change history, and retirement.
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 ensure consistent telemetry 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.
Design monitoring for control failures, model drift, anomalous use, permission breaches, and high-risk actions, with clear escalation and remediation paths.
Build dashboards and evidence packs for service owners, Risk, Infosec, and Internal Audit.
Work with Risk, Infosec, Legal, Compliance, Privacy, Records Management, and TPRM to understand needs and convert them into technical requirements.
Write specific standards and control requirements, and explain platform behavior and rationale to non-engineers.
Advise AI Architecture, AI Engineering, AI Platforms, and Forward Deployed Engineering on control design and use case classification.
Support risk-based onboarding of new foundation models, AI software, and connectors, and transfer operating knowledge.

Benefits

Hybrid working and reasonable accommodations
Generous Holiday policies
Excellent Health and Wellbeing benefits including corporate membership to Wellhub
Paid volunteer time
Support to grow through professional development courses, tuition/qualification reimbursement and more
Maternal/paternal leave benefits and family services
Unique employee events and programs
Complimentary beverages, snacks and all employee Happy Hours
Competitive compensation
Pension/retirement plans
Various health, wellbeing and lifestyle benefits
Annual discretionary bonus award from the profit pool