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About This Role
City: Denver Division: Office of the CISO
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 is a hands-on security engineering role for people who are deep in cybersecurity and serious about AI. You will secure the AI systems we are building — AI-enabled applications, models, agents, and the platforms beneath them, across their full lifecycle — acting as a trusted advisor who helps define and evolve the firm’s AI security standards, patterns, and guardrails. The objective is not to create additional process, but to ensure our existing security capabilities evolve alongside AI adoption and remain effective at enterprise scale.
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 builds and runs it. This role sits in Information Security and reports to the Head of Security Engineering, while your day-to-day work is directed by AI Technology and prioritised against their roadmap. That split is deliberate: security policy, security architecture approval, and security risk acceptance stay with Infosec, and you are the person who exercises them for AI — close enough to the engineering to be useful, independent enough to say no.
You will secure Nexus, our agentic workspace where employees and citizen developers build and run AI applications, agents, and shared skills; Accio, our centralised MCP server, which consumes other MCP servers and presents enterprise datasets through one governed interface; the AI model gateway and its routes to external model providers; and our Copilot services. Accio and Nexus matter most: both propagate permissions and data on a user’s or an agent’s behalf, so their trust boundaries have to hold when a request crosses several hops. Throughout, you will balance effective risk management against the velocity needed to deliver AI solutions that drive business value and growth.
A secondary focus of the role is partnering with the wider Security function to identify opportunities to leverage AI and automation to improve efficiency, effectiveness, and risk reduction across security operations, engineering, assurance, and governance — so that the controls you design are enforced and evidenced automatically rather than through review meetings.
What success looks like
- Security is designed into AI systems rather than appended. Engineers reach for an approved pattern instead of asking for a bespoke review.
- The controls you define are implemented as code and secure defaults by AI Engineering and AI Platforms, and you can show they are operating.
- AI-specific attacks are anticipated and tested for, and you can demonstrate what the firm is and is not exposed to.
- New models, tools, and Copilot features reach a security decision quickly through a repeatable risk-based path, and when an AI incident happens the lesson lands in a platform default rather than a report.
- Risk reduction and control effectiveness are evidenced through agreed KPIs, KRIs, and reporting rather than asserted.
Your responsibilities Secure the AI estate by design
- Act as security design authority for AI systems, leading threat modelling, architecture review, and risk assessment for agentic applications, the model gateway, Accio, Nexus, and any novel or high-risk AI initiative, applying STRIDE, PASTA, MITRE ATT&CK, and MITRE ATLAS as appropriate.
- Define and evolve AI security standards, guardrails, governance controls, and secure-by-design patterns aligned with enterprise requirements and the firm’s risk appetite — co-designed with the Principal AI Architect as reusable, implementable guardrails, and implemented as code and secure defaults by AI Engineering and AI Platforms, working with the AI Governance Implementation Lead, who owns how they land on the platform.
- Design identity, entitlement, and secrets patterns for non-human identities — agents, tools, MCP servers, connectors, and service principals — with the Senior AI Platform Engineer and enterprise IAM, covering scoped permissions, credential lifetime, rotation, and least privilege across multi-hop requests.
- Set the trust boundaries and data-egress controls for the AI estate, including what may reach external model providers, and work with data protection owners on classification, DLP enforcement, privacy, and data governance obligations.
Test, detect, and respond
- Run adversarial testing and AI red teaming against models, prompts, agents, and tool chains, covering prompt injection and indirect injection, model manipulation and hijacking, excessive agency, function-call abuse, data leakage from LLM outputs, and privilege escalation between agents.
- Build detections, security analytics, and telemetry for AI-specific abuse — anomalous tool invocation, credential misuse by agents, unusual data access, and exfiltration through model responses — and integrate them into the firm’s monitoring estate.
- Support security incident response for AI systems: triage, containment, forensics across prompts, tool calls, and agent decisions, and root cause, feeding each lesson back into platform defaults and evaluation suites.
- Own security testing in the AI delivery lifecycle, including static analysis, dependency and container scanning, secrets detection, and security gates in CI/CD, so issues surface before release.
Assure AI adoption and third-party risk
- Co-own the risk-based security gate for onboarding new AI products, model providers, versions, and platform features with the Senior AI Platform Engineer and the AI Governance Implementation Lead, conducting security due diligence and risk assessment of AI vendors, platforms, and models — provenance, open-source components, tenancy and data-use terms, security advisories — and testing platform updates before rollout.
- Decide with the Senior AI Platform Engineer and the Principal AI Architect which Copilot features are released and to whom, withholding capability until the required controls are evidenced; review the solutions Forward Deployed Engineering builds and the platforms built with Percepta so neither reaches production without a security position; and set the guardrails for citizen developers and Copilot Studio makers with AI Enablement.
- Support Risk and Internal Audit with security evidence, exercise Infosec’s security-control approval and risk-acceptance position for AI, and escalate where residual risk exceeds appetite.
Scale the security function with AI and automation
- Identify and deliver opportunities to apply AI and automation across security operations, engineering, assurance, and governance, building automation with code, APIs, scripting, orchestration platforms, or low-code technologies to automate response workflows, enrich incident data, assist with triage, and drive risk-based vulnerability management.
- Define and report the KPIs, KRIs, dashboards, and reporting that demonstrate risk reduction, control effectiveness, and operational improvement.
- Mentor security engineers on AI security and build enough AI literacy across Infosec that this role is not a single point of knowledge.
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
- Strong cybersecurity experience across security engineering, application security, product security, cloud security, or security architecture, with a hands-on engineering background, a track record of protections that reached production, and a solid grasp of security engineering fundamentals — common attack vectors, defence techniques, and threat modelling.
- Hands-on experience assessing and securing GenAI, LLM, machine learning, agentic AI, and AI-enabled solutions throughout their lifecycle.
- Proven ability to lead threat modelling, architecture reviews, and risk assessments for complex technology platforms and services.
- Strong understanding of AI-specific threats, threat modelling methodologies, adversary frameworks, and risk assessment approaches, including prompt injection, model manipulation, excessive agency, STRIDE, PASTA, MITRE ATT&CK, MITRE ATLAS, and equivalent industry practices.
- Experience defining and evolving AI security standards, guardrails, governance controls, and secure-by-design patterns aligned with enterprise requirements and risk appetite.
- Experience securing AI agents, MCP integrations, permissions, non-human identities, autonomous workflows, and AI platform integrations.
- Cloud security depth, ideally Azure — identity and access management, service principals and workload identity, secrets management, RBAC, network controls, and logging — and experience securing application delivery, including secure SDLC, CI/CD controls, and code and dependency scanning.
- Practical experience with AI and LLM systems — prompt engineering, retrieval-augmented generation, function calling, agent-based tools — and proven ability to build and deploy automation using code, APIs, scripting, orchestration platforms, or low-code technologies such as Python, workflow engines, or SOAR, integrating security logs, AI models, and platform components into cohesive pipelines.
- Metrics-driven mindset, with experience defining KPIs, KRIs, dashboards, and reporting to demonstrate risk reduction, control effectiveness, and operational improvement.
- Strong stakeholder engagement and influencing skills, with the ability to translate technical risk into business impact, pragmatic controls, and informed risk decisions — and the independence to hold a security position under delivery pressure.
Nice to have skills
- Familiarity with AI security and governance frameworks including NIST AI RMF, the OWASP Top 10 for LLM applications, MITRE ATLAS, ISO/IEC 42001, and the security provisions of the EU AI Act.
- AI red teaming, adversarial testing, adversarial machine learning, model validation, or offensive security assessments applied to models and tool chains.
- Experience securing enterprise AI platforms, model gateways, AI development environments, and AI engineering ecosystems.
- Knowledge of AI security posture management, runtime protection, model monitoring, and AI governance tooling.
- Knowledge of identity security, including IAM, PAM, identity governance, privileged access management, non-human identities, and workload identities.
- Experience applying AI and automation to vulnerability management, detection engineering, threat detection, or security operations at scale, including risk-based threat prioritisation.
- Knowledge of security analytics, monitoring, and detection capabilities for AI systems and supporting infrastructure.
- Understanding of privacy, data governance, regulatory, and responsible AI considerations, including Microsoft Purview, DLP policy design, or data classification across a Microsoft 365 estate.
- Securing agentic workflows, including scoped permissions, approval patterns, and guarding against configuration drift caused by AI assistants.
- Third-party or model supply-chain risk assessment; financial services or asset management experience; or a certification such as CISSP, GIAC, OSCP, or a cloud security qualification.
Supervisory responsibilities No. This is an individual-contributor role reporting into Information Security, with its work directed by AI Technology. It carries security design authority for AI systems and mentors security engineers, 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 $145,000 - $190,000. 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 $145K-$190K 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $145K to $190K.
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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