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About This Role
City: Denver Division: Corporate - CFO
Why work for us?
A career at Janus Henderson is more than a job, it’s about investing in a brighter future together.
Our Mission at Janus Henderson is to help clients define and achieve superior financial outcomes through differentiated insights, disciplined investments, and world-class service. We will do this by protecting and growing our core business, amplifying our strengths and diversifying where we have the right.
Our Values are key to driving our success, and are at the heart of everything we do:
Clients Come First - Always | Execution Supersedes Intention | Together We Win | Diversity Improves Results | Truth Builds Trust
If our mission, values, and purpose align with your own, we would love to hear from you!
Your opportunity Janus Henderson is undertaking a firm-wide AI transformation to become the most technologically sophisticated asset manager in the industry, and it has to move quickly without compromising security, resilience, or regulatory obligations. Our AI capability sits in a single centralised function under the Head of AI. AI Technology owns the AI product and workload plane — the software, models, gateways, and controls used to build and run AI across the firm — and AI Platforms is the team within it that owns the tooling estate itself.
As Senior AI Platform Engineer, reporting to the Head of AI Technology, you will own the day-to-day engineering and lifecycle of that estate, from Azure AI Foundry and the AI model gateway through to Claude and our Copilot services. You will assess new products, run controlled pilots, automate onboarding, and turn selected tools into supported services. You will also hold the administrator seat for Microsoft 365 Copilot, GitHub Copilot, and Copilot Studio, controlling their administrative settings and the plan by which they are deployed.
This is a hands-on role with broad ownership, working across AI Engineering, AI Architecture, AI Governance Implementation, AI Security, Central Technology, Procurement, and the AI Enablement Partners. You will be expected to understand both the code and the contract: how a tool integrates, how it behaves in production, what it costs, and how it is controlled. The portfolio will change quickly, so the role needs sound judgement and the ability to turn one-off approval work into a repeatable onboarding path.
What success looks like
- New AI products reach a decision quickly through a repeatable risk-based path, and teams self-serve — engineers and business users consume approved tools and models through documented patterns rather than one-off requests to you.
- The model gateway is dependable. Availability, latency, quotas, and cost are visible, and provider change is absorbed without breaking the teams that consume it.
- Copilot capability lands deliberately, with licences, cohorts, and features released to a plan and the required controls evidenced first.
- Spend is defensible. Token consumption and licence take-up are attributed by team, agent, and provider, budgets are enforced where they need to be, and you act on what is underused or duplicated.
Your responsibilities Engineer and operate the AI tooling estate
- Build, configure, and operate shared AI software and platform services used by engineering teams, business users, agents, and applications.
- Own the technical lifecycle of AI tools from discovery and pilot through onboarding, upgrades, support, renewal, and retirement.
- Automate provisioning, configuration, policy enforcement, and evidence collection using APIs, infrastructure as code, and CI/CD.
- Provide L2 support, maintain runbooks and recovery procedures, and escalate to vendors or Central Technology where the issue sits outside the AI workload plane.
Run the model gateway and onboard AI software
- Engineer and operate the AI Team’s model gateway and routes to hosted and external model providers, managing integrations, credentials, quotas, routing rules, fallback, and rate limits.
- Implement identity, role-based access, entitlements, and secure secret handling with enterprise IAM and AI Security, and monitor availability, latency, errors, usage, and policy breaches with auditable records of model access, decisions, and costs.
- With AI Security and AI Governance Implementation, establish a risk-based process for evaluating and onboarding new AI products, model providers, versions, and platform features, leading the technical discovery, pilots, and production-readiness reviews, and distinguishing routine upgrades from materially new or higher-risk capabilities.
- Work with AI Architecture on buy-versus-build decisions and encode approved controls into the platform.
Administer and deploy our Copilot services
- Act as service owner and administrator for Microsoft 365 Copilot, GitHub Copilot, and Copilot Studio, controlling every administrative setting and policy through delegated administrator roles in the tenants Central Technology operates.
- Own the deployment plan: licence assignment, cohort sequencing, environment and connector strategy, agent publishing routes, and the order in which capability reaches each part of the firm.
- Define the Copilot roadmap with the Principal AI Architect and AI Security, deciding which vendor features are made available and to whom, and staging capability until the required controls are evidenced.
- Govern the Copilot Studio maker experience — environments, data-loss-prevention policies, connectors, and publishing approval — so business users build agents inside agreed guardrails.
Own telemetry, token budgets, licences, and vendors
- Own and maintain the usage, cost, and token telemetry across every AI system — the model gateway, Copilot services, Azure AI Foundry, and external providers — so consumption is attributed consistently to users, teams, agents, applications, and providers, and keep it accurate as the estate changes.
- Administer token budgets at the granularity the firm needs, whether by user, team, agent, application, or use case. Set, adjust, and enforce allowances through quotas, alerts, and access boundaries, and give budget owners the visibility to manage their own consumption.
- Report on adoption, licence take-up, spend, and service quality in enough detail to support funding decisions, and identify underused licences and duplicated products.
- Act as technical owner for AI software vendors, supporting selection, due diligence, licensing, renewals, and exit planning with Procurement, Legal, TPRM, and Finance, and track vendor roadmaps, deprecations, and security advisories.
- Work alongside Percepta and other delivery partners while new platforms are built, and accept platforms into service only when ownership and controls are clear.
- Partner with AI Engineering and Forward Deployed Engineering so teams self-serve approved tools and models; with AI Governance Implementation, Risk, and AI Security to turn policy into automated controls; with AI Architecture to stay aligned to the target architecture; and with the AI Enablement Partners so training matches what the platform permits.
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
- Unique employee events and programs including a 14er challenge
- Complimentary beverages, snacks and all employee Happy Hours
Must have skills
- At least five years in platform engineering, software engineering, DevOps, SRE, or cloud engineering, including building and operating shared services used by multiple teams.
- Strong automation skills in Python or a comparable language, and hands-on experience with a major cloud, ideally Azure, including infrastructure as code, CI/CD, identity, RBAC, and secret management.
- Production experience with LLM APIs or AI platforms, covering model access, quotas, rate limits, observability, and provider change management.
- Hands-on administration of a major enterprise SaaS or productivity platform at tenant scale, including policy configuration, licence and entitlement management, staged feature enablement, and phased rollout to a large user base.
- Experience acting as technical owner for software vendors, covering selection, due diligence, licensing, renewals, and cost management.
- Good judgement in a regulated environment, translating security, privacy, risk, and audit requirements into working technical controls, and explaining trade-offs clearly to engineers, control functions, and senior stakeholders.
- High ownership and comfort with change. You can bring structure to an unfamiliar product, resolve dependencies across teams, and remain accountable after it enters production.
Nice to have skills
- Azure AI Foundry, Azure OpenAI, Anthropic Claude, Microsoft 365 Copilot, GitHub Copilot, or Microsoft Copilot Studio.
- Microsoft 365, Entra ID, Purview, and Power Platform administration, including admin-centre configuration, data-loss-prevention policies, and environment strategy.
- Experience building or operating an AI gateway, inference-routing layer, or multi-provider model platform.
- Terraform, Docker, Kubernetes, or Azure DevOps, and enterprise observability, ITSM, or FinOps platforms.
- Financial-services or asset-management experience, including regulated data and third-party risk processes.
Supervisory responsibilities No. This is a senior individual-contributor role. The Senior AI Platform Engineer leads technical work and coordinates delivery across internal teams and vendors, but does not line-manage.
Potential for growth
- Mentoring
- Leadership development programs
- Regular training
- Career development services
- Continuing education courses
Compensation information
The base salary range for this position is $195,000 - $230,000. This range is estimated for this role. Actual pay may be different. This position will be open through [add date]
Colorado law requires an estimated closing date for job postings. Please don't be discouraged from applying if you see this date has passed.
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! We understand everyone has different commitments and while we can’t accommodate every flexible working request, we’re happy to be asked about work flexibility and our hybrid working environment. If you need any reasonable accommodations during our recruitment process, please get in touch and let us know at [email protected].
Annual Bonus Opportunity: Position may be eligible to receive an annual discretionary bonus award from the profit pool. The profit pool is funded based on Company profits. Individual bonuses are determined based on Company, department, team and individual performance.
Benefits: Janus Henderson is committed to offering a comprehensive total rewards package to eligible employees that includes; competitive compensation, pension/retirement plans, and various health, wellbeing and lifestyle benefits.
Janus Henderson Investors is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. All applications are subject to background checks.
Janus Henderson (including its subsidiaries) will not maintain existing or sponsor new industry registrations or licenses where not supported by an employee’s job functions (as determined by Janus Henderson at its sole discretion).
You should be willing to adhere to the provisions of our Investment Advisory Code of Ethics related to personal securities activities and other disclosure and certification requirements, including past political contributions and political activities. Applicants’ past political contributions or activity may impact applicants’ eligibility for this position.
You will be expected to understand the regulatory obligations of the firm, and abide by the regulated entity requirements and JHI policies applicable for your role.
Nearest Major Market: Denver
Salary Context
This $195K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Janus Henderson Investors, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $195K to $230K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Janus Henderson Investors AI Hiring
Janus Henderson Investors has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, Denver, CO, US, London, England, UK. Compensation range: $190K - $230K.
Location Context
AI roles in Denver pay a median of $197,900 across 126 tracked positions. That's 7% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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