Interested in this AI/ML Engineer role at Janus Henderson Investors?
Apply Now →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 programme to become the most technologically sophisticated asset manager in the world — an ambition that depends on having the most technologically sophisticated people to match it. The AI Academy is the upskilling and cultural-change pillar of that transformation, sitting alongside the Build and Buy workstreams. In its first six months, AI Academy 2.0 has reached enterprise scale. Following a firm-wide launch during Careers Week, adoption has increased to around 86% and monthly prompt volumes have quadrupled. Firm-wide AI 101 completion sits above 90%, a network of departmental champions now covers every business unit, a cohort of 200-plus advanced users is in place, and the programme is supported by a refreshed Academy homepage, a Use Case Library, an embedded Responsible AI framework, and a regular communications cadence.
The Head of AI Academy owns this function and leads its next phase. Reporting to the Head of AI Enablement, you will take end-to-end ownership of the Academy — its strategy, curriculum, proficiency pathways, champion networks, platform, community, measurement and governance — and the small central team and specialist contributors that deliver it. The strategic challenge has shifted from access to depth: moving the firm from broad enablement to role-based capability, advanced-tool proficiency and stronger guardrails, and positioning the Academy as the adoption layer for the firm’s next horizon of AI — the enterprise tools it is building and buying as well as those still to come.
This is a leadership role at the intersection of capability building, stakeholder engagement, AI literacy and change enablement. You will be equally comfortable setting curriculum direction, coaching a network of departmental champions, influencing senior stakeholders and executive sponsors, and holding the firm to a credible, evidence-led view of what “good” adoption looks like. You will operate within the AI Academy framework alongside the AI Enablement Partner function, the champions and power users, and partner closely with Technology, our build partner (Percepta), AI Governance, Risk, Compliance and Learning & Development.
Own and evolve the AI Academy strategy and operating model* Own the AI Academy strategy end-to-end as the upskilling and cultural-change pillar of the firm’s AI transformation, keeping it tool-agnostic and aligned to the firm’s ambition and horizons.
- Evolve the operating model that combines decentralised business ownership, local capability, embedded delivery (AI Enablement Partners) and central pathways, standards, advanced training and community.
- Move the programme from broad access to depth of use — role-based capability, repeatable workflows and stronger guardrails — and set the roadmap for Tier 2 and Tier 3 advanced pathways.
Lead the proficiency pathways and curriculum* Own and continuously improve the three-tier proficiency journey and the learning that underpins it.
- Direct the core curriculum, including AI 101 (tool-agnostic foundations) and Copilot 101 (hands-on, department-specific delivery), and relaunch and run the advanced programme for power users.
- Build advanced, role-specific content that closes the training ceiling advanced users report — prompting craft, Copilot Studio and agent design, Excel and productivity depth, and hallucination and governance guardrails.
Lead the Air Traffic Controller and Fighter Pilot networks* Lead and grow the ATC network (departmental AI leads) and the Fighter Pilot cohorts, running the train-the-trainer model that cascades capability into every team.
- Set standards, protected-time expectations and ways of working for the networks, and secure line-manager accountability for the agreed ATC time commitment.
- Coach, develop and recognise champions, and build the community and peer learning that keeps them engaged and effective.
Own the Academy platform, content and community* Own the AI Academy homepage and its sections — Overview, Learning Pathways, Community & News, Department Guidance and Responsible AI — as the single, trusted home for AI enablement.
- Own the Use Case Library and Prompt Library, driving contribution, curation and reuse of proven patterns across departments, and consolidate AI training material into one channel and one brand.
- Run the communications cadence — including the AI newsletter and the innovation community feed — and improve its relevance, engagement and discoverability.
Measurement, scorecard and reporting* Own the adoption dashboard and the Academy’s contribution to the ExCo scorecard, and evolve metrics from licence assignment and active use toward proficiency and depth of engagement.
- Run structured user research (for example the quarterly interview corpus) to map demand, pain points and training needs, and prioritise the roadmap on the evidence.
- Report progress, risks and decisions into the firm’s AI transformation governance, distinguishing clearly between prerequisites (access) and outcomes (value).
Responsible AI, governance and enterprise adoption* Embed the firm’s Responsible AI principles — Own the Output, Review Before Sharing, No Sensitive Data, Judgement First, Approved Tools Only, Usage Monitored — across all Academy content and pathways.
- Partner with AI Governance, Technology, Security, Risk and Compliance so that tools taught and patterns shared stay within the firm’s data-privacy, security and responsible-AI posture.
- Position the Academy as the adoption layer for the firm’s next horizon — enabling the safe, effective take-up of enterprise tools and agents the firm builds and buys, and leading the change management that goes with them.
Leadership and cadence* Lead the central Academy team and specialist contributors (delivery, content and community), setting direction, standards and a high bar for quality.
- Chair the Academy governance forum and the monthly ATC catch-up, and represent the Academy in the firm’s AI transformation leadership forums.
- Carry out other duties as assigned.
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
- Proven leadership experience — building, leading and developing teams and/or distributed networks of champions across multiple business units, ideally in an enablement, capability or change context.
- Strong, practical understanding of AI and GenAI concepts, capabilities and limitations, with the ability to translate them for non-technical audiences and set a credible direction for the programme.
- Hands-on familiarity with the firm’s core toolkit — Microsoft Copilot and the M365 ecosystem, Copilot Studio and adjacent tooling — and a genuine appetite to evaluate, learn and deploy new tools as they emerge.
- Demonstrated experience designing and running training, curriculum or capability-building programmes at scale, and the judgement to tell apart a session people endure from one they apply the next morning.
- Excellent stakeholder management and influencing skills, with credibility across business and senior audiences and the ability to lead through influence where contributors are embedded in departments.
- Practical change-management and adoption experience — you understand that access and training are only half the job, and that surfacing and removing adoption blockers is the other half.
- Strong measurement literacy — comfortable designing surveys, interpreting adoption data and building scorecards, and disciplined about the difference between adoption (a prerequisite) and value (the goal).
- Self-directed and comfortable operating with ambiguity — the Academy is being scaled and reshaped, and you will define how much of it runs, with sponsorship but without a detailed playbook.
- Strong documentation instincts — you produce reusable curriculum, pathways and playbooks that outlast your direct involvement and can be picked up by others.
- Experience working in a regulated environment (financial services, asset management or comparable) where governance, compliance and data sensitivity shape how solutions are designed and deployed.
Nice to have skills
- Direct experience in asset management or financial services — exposure to investment, distribution, operations or client-service workflows.
- Experience with AI governance, risk and responsible-AI practices, and familiarity with the emerging regulatory landscape (e.g., EU AI Act).
- Familiarity with Copilot Studio, the Power Platform, agentic tooling and the ability to understand how these tools fit in with our evolving internal AI platforms.
- Background in a community-of-practice, guild or cohort-based enablement model, or in internal communications and community building.
- Product-management sensibility — MVPs, iteration, user feedback and prioritisation — applied to curriculum and enablement rather than software features.
- Experience managing vendor or staff-augmentation partnerships that flex delivery capacity.
Supervisory responsibilities
- No
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 USD 170,000-210,000. This range is estimated for this role. Actual pay may be different. This position will be open through November 15, 2026.
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]. #LI-LN2 #LI-Hybrid 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 $170K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $170K to $210K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Janus Henderson Investors AI Hiring
Janus Henderson Investors has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, Budapest, Budapest, Hungary, London, England, UK. Compensation range: $210K - $210K.
Location Context
AI roles in Denver pay a median of $200,000 across 134 tracked positions. That's 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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