Interested in this AI/ML Engineer role at Janus Henderson Investors?
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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. Our AI capability sits in a single centralised function under the Head of AI, and AI Technology is the part of it that builds, governs, and runs the software.
AI Engineering is the core product and platform engineering team within it. Where Forward Deployed Engineering embeds with a business unit to solve one team’s problem, AI Engineering builds the enterprise products every other team depends on: Nexus, our agentic workspace, where employees build, test, run, and manage governed AI applications, agents, and shared skills; Accio, our centralised MCP server, which acts as a passthrough and logic centre for enterprise datasets, consuming other MCP servers and presenting them through one governed interface; and the orchestration, evaluation, and observability services beneath Libros and PRISM, two of the projects we are delivering with Percepta.
As a Senior Applied AI Engineer, reporting to the Principal AI Engineer, you will lead the delivery of major parts of that estate — designing and building agentic applications and platform services, taking them through evaluation and our AI governance checkpoints into production, then owning them once live. You will set implementation patterns alongside AI Architecture, review other engineers’ work, and be the person the team turns to when a production system behaves in a way nobody predicted. You will also work beside Percepta’s engineers, making sure each platform enters service with a design the team understands and more than one person able to extend it.
You will also own the end-to-end build of business applications for specific parts of the firm — not only the horizontal products every team shares, but targeted applications built on the AI stack for a named business area, carried from first requirement through to a supported production service. Distribution is the first focus. Much of this work is deliberate SaaS decommissioning: where a capability currently bought as a SaaS subscription can be built in-house instead, you own that build end-to-end and see the displaced product retired, so the firm consolidates onto governed, in-house capability rather than paying for overlapping tools. You will take the same approach into other business areas — trading, investment risk, client servicing, and operations among them — choosing the builds where owning the application gives the firm more control, lower cost, or capability no vendor sells.
Your toolkit spans Python and SQL, LLMs and agent frameworks, MCP, Azure AI Foundry through the AI Team’s model gateway, Snowflake and Microsoft Fabric, and Azure with Terraform, Docker, and CI/CD. AI Engineering is being established for the first time here, so you will shape how the team works rather than inherit a settled routine.
What success looks like
- The products and services you own are in production, used by real teams, and operate with clear ownership, telemetry, and evaluation results.
- What you build gets reused. Other engineers, agents, and applications consume it through Accio or as a skill, tool, or connector on Nexus rather than building their own.
- Platforms built jointly with Percepta transfer into our ownership with maintainable designs and a team that can extend them without external help.
- Evaluation, observability, and control are built into what you ship rather than bolted on before release.
Your responsibilities Build and run our AI products and platform
- Design, build, and productionise AI products and shared platform services, taking a workstream end to end and owning it through to live running.
- Build for reuse, turning capability proven in one product into shared components, libraries, skills, tools, MCP servers, and connectors.
- Implement infrastructure as code, own CI/CD pipelines, and accept services into operation only when ownership, controls, and support are clear.
- Investigate incidents and defects in the services you own, lead recovery, carry fixes through to the underlying cause, and act as L3 escalation.
Engineer agents, orchestration, and the application layer
- Build agentic applications and workflows covering agent design, prompt and context engineering, tool use, memory, retrieval, and human-in-the-loop controls.
- Implement model selection, routing, and fallback through the model gateway, handling provider change without hiding differences in capability, cost, or behaviour.
- Build the orchestration layer that long-running and multi-step agents depend on, covering permissions, workload isolation, safe execution, and the applications built on it.
- Test the claims made for new models, frameworks, and patterns before recommending we adopt them.
Make enterprise data AI-ready
- Build governed, AI-ready views, indexes, semantic context, and connectors over Snowflake, enterprise platforms, APIs, and external providers.
- Extend Accio so new datasets and downstream MCP servers are reachable through one governed interface rather than one-off integrations.
- Build ingestion pipelines and data models where source data does not arrive usable, while canonical source ownership stays with the relevant Technology team.
- Own lineage, quality, and freshness for the data your products depend on, and implement least-privilege access for users, agents, tools, and service identities.
Build business-area applications and decommission SaaS
- Own the end-to-end build of targeted business applications on the AI stack — Distribution first, then areas such as trading, investment risk, client servicing, and operations — delivered for a named business owner rather than for every team at once.
- Drive SaaS decommissioning: where a bought subscription can be replaced by an in-house build, own the delivery of that replacement end-to-end, see the displaced product retired, and work with Procurement and Finance to consolidate the licence.
- Prioritise the builds where in-house ownership gives the firm more control, lower cost, or capability no vendor sells, and reuse existing Accio datasets, skills, and connectors instead of rebuilding them per application.
- Take each application through the same evaluation, controls, and release evidence as the shared products, and own its support and lifecycle once live or hand it to a clear owner.
Build in evaluation and control, and work with the team
- Build evaluation and regression suites for models, prompts, agents, and platform changes, meet the agreed thresholds before release, and instrument what you build for quality, safety, reliability, latency, drift, usage, and cost.
- Implement the controls defined by AI Governance Implementation and AI Security as code and secure defaults, and produce release evidence proportionate to the risk of the use case, generated by the platform rather than assembled after the event.
- Work alongside Percepta engineers on Libros, PRISM, and the wider estate, holding joint work to our engineering standards and raising maintainability problems while they are still cheap to fix.
- Mentor AI Engineers through pairing, design discussion, and code review, and help establish the team’s engineering standards, agentic SDLC, and definition of done.
- Work with AI Architecture to turn reference patterns into implementations teams use, partner with Forward Deployed Engineering so proven solutions become supported shared capability, and feed adoption data from the AI Enablement Partners into the backlog.
Must have skills
- At least six years in software, data, or platform engineering, with a track record of shipping and operating production systems rather than prototypes.
- Production experience with LLM applications and agentic systems, covering prompt and context engineering, agent development, retrieval, tool use, evaluation, and deployment at scale.
- Strong Python and SQL, with the judgement to write code others can maintain and extend.
- Hands-on experience building agentic capability such as MCP servers, tools, skills, or connectors for other systems and agents to consume.
- Hands-on experience with a major cloud, ideally Azure, including containers or serverless compute, infrastructure as code, CI/CD, identity, RBAC, and secret management.
- Experience owning services in production — monitoring, incident investigation, upgrades, lifecycle management — and establishing evaluation and observability for AI systems.
- Good judgement in a regulated environment, translating security, privacy, risk, and audit requirements into working technical controls.
- Experience mentoring engineers and leading technical work without relying on reporting authority, and clear communication with engineers, control functions, and business stakeholders.
Nice to have skills
- Asset management or financial services domain knowledge, particularly the investment process, distribution, or front-office workflows.
- Azure AI Foundry, Azure OpenAI, Anthropic Claude, model gateways, inference routing, or agent orchestration platforms.
- Snowflake, Microsoft Fabric / OneLake, or comparable governed enterprise data platforms.
- TypeScript or a second production language, and front-end experience for user-facing AI applications.
- Experience building internal developer platforms or golden-path patterns used by other engineering teams, or taking partner-built software into internal 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
- Unique employee events and programs including a 14er challenge
- Complimentary beverages, snacks and all employee Happy Hours
Supervisory responsibilities No. This is a senior individual-contributor role. The Senior Applied AI Engineer leads technical delivery, sets implementation standards, and mentors AI Engineers, but does not line-manage. The role may act as technical lead for a product or platform workstream.
Compensation information
The base salary range for this position is $130,000 - $220,000. 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 $130K-$220K range is below 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. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $130K to $220K.
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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