Interested in this AI/ML Engineer role at Janus Henderson Investors?
Apply Now →About This Role
City: Denver
Division: Technology Delivery
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
This role sits within the Office of the Chief Technology Officer function of Janus Henderson Investors’ Global Technology organization. Global Technology consists of \~450 employees across the UK, the US, Australia, and other APAC countries.
Janus Henderson has adopted Agile ways of working to support the effective delivery of technology and business change across its Value Streams.
The Delivery Enablement Lead is responsible for enabling successful delivery across multiple teams, products and technology domains. This is achieved by driving alignment, removing impediments, managing dependencies and providing visibility of delivery performance. The role operates primarily at a Value Stream Level, partnering with Product Managers, Product Owners, Scrum Masters, Technology Leads and senior stakeholders to ensure strategic initiatives are delivered effectively and efficiently.
The Delivery Enablement Lead acts as a servant leader and delivery facilitator, helping teams maintain momentum while driving operational excellence, governance and continuous improvement across the organisation.
In this role, you will play key role in:
AI Strategy, Governance and Executive Support
- Support the AI Enablement Lead in preparing for and coordinating key governance and leadership forums.
- Develop executive\-quality briefing materials, agendas, decision papers and action tracking.
- Track strategic AI initiatives and provide visibility of progress, risks, dependencies and decisions.
Leadership and Representation
- Deputise for the Head of AI Engineering in governance forums, executive meetings and stakeholder engagements.
- Represent the AI Engineering function across Technology and Business leadership meetings.
- Build trusted relationships with stakeholders across Technology, Risk, Security, Legal, Data and Business functions.
- Influence and facilitate executive decision\-making to remove blockers and maintain momentum across AI initiatives.
AI Programme Delivery
- Coordinate delivery of strategic AI initiatives
- Monitor delivery milestones, dependencies, risks and outcomes across multiple AI initiatives.
- Support operational readiness and successful adoption of AI\-enabled solutions.
Stakeholder Engagement and Decision Enablement
- Facilitate decision\-making sessions with senior business and technology leaders.
- Prepare recommendations and options papers to support informed executive decisions.
- Build consensus across diverse stakeholder groups where priorities compete.
- Escalate delivery, governance or organisational issues appropriately and drive timely resolution.
AI Governance and Risk Management
- Support implementation and continuous improvement of AI governance frameworks.
- Coordinate governance activities relating to AI tooling, model usage, risk, compliance and responsible AI practices.
- Maintain visibility of key AI risks, issues, dependencies and mitigation plans.
- Ensure governance decisions are documented, communicated and embedded across programmes.
Cross\-Functional Coordination
- Coordinate activities across Technology, Data, Security, Infrastructure, Architecture, Risk, Legal and Procurement teams.
- Promote collaboration across business and technology functions to enable successful AI adoption.
- Identify organisational dependencies and facilitate resolution of cross\-functional issues.
Continuous Improvement
- Identify opportunities to improve AI governance, delivery practices and operating models.
- Promote effective ways of working across AI programmes and governance forums.
- Share lessons learned and embed best practice across AI initiatives.
- Support continuous evolution of the organisation’s AI Enablement capability.
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
- Experience supporting AI Platform \& Tooling Strategy and Governance for model integration layers, and developer tooling (e.g., Copilot, prompt management, evaluation pipelines)
- Demonstrable experience delivering AI transformation programmes in complex organisations.
- Experience supporting executive governance and preparing senior leaders for strategic meetings.
- Proven ability to deputise for senior leaders and represent organisational priorities with credibility.
- Strong track record of influencing senior stakeholders and facilitating executive decision\-making.
- Experience managing cross\-functional programmes involving Technology, Data, Security and Business teams.
- Strong organisational, planning and stakeholder management skills.
- Experience managing risks, dependencies and strategic delivery issues.
- Excellent facilitation, negotiation and communication skills.
- Familiarity with Agile delivery methodologies and portfolio governance.
- Experience operating within regulated or large enterprise environments.
- Familiarity with agent\-orchestration layers, model\-evaluation harnesses, or LLM\-backed product features at production scale
Nice to have skills
- Experience in Financial Services or related field
- Knowledge of risk, compliance, and governance frameworks relevant to regulated industries.
- Experience with enterprise AI governance frameworks and Responsible AI practices.
- Experience operating within regulated or large enterprise environments.
- Experience with Agile delivery tooling, including Jira and associated reporting platforms.
- SAFe RTE certification or equivalent Agile qualifications desirable.
Supervisory responsibilities
- No
Potential for growth
- Mentoring
- Leadership development programs
- Regular training
- Career development services
- Continuing education courses
Compensation information
The base salary for this position is $145,000 \- $160,000\. This salary is estimated for this role. Actual pay may be different. This range will be posted through July 9, 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.
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.
At Janus Henderson Investors we’re committed to an inclusive and supportive environment. We believe diversity improves results and we welcome applications 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 $145K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($152K) sits 30% below the category median. Disclosed range: $145K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Janus Henderson Investors AI Hiring
Janus Henderson Investors has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $160K - $160K.
Location Context
AI roles in Denver pay a median of $201,050 across 48 tracked positions. That's 8% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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