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About This Role
BeyondTrust is a place where you can bring your purpose to life through the work that you do, creating a safer world through our cybersecurity SaaS portfolio.
Our culture of flexibility, trust, and continual learning means you will be recognized for your growth, and for the impact you make on our success. You will be surrounded by people who challenge, support, and inspire you to be the best version of yourself.
The Role
The Senior Director, AI Strategy \& Architecture serves as the company's most senior internal AI technical advisor and strategist responsible for designing, building, and scaling AI\-enabled capabilities across the organization. Reporting directly to the SVP of Information Systems, this high\-visibility individual contributor role operates across the entire enterprise — spanning Engineering, Sales, Marketing, Operations, Finance, Legal, HR, and Customer Success — with the mandate to operate as the connective layer between business teams, cross\-functional AI initiatives, and underlying technology platforms — ensuring that AI solutions are built correctly, scale effectively, and align with enterprise strategy driving measurable business value.
Success depends on the ability to translate business problems into functional AI solutions, move rapidly from concept to production, and build reusable capabilities that prevent duplication and accelerate adoption across BeyondTrust.
This role builds on an existing foundation of departmental AI momentum — you will work alongside leaders already running AI initiatives within their teams, serving as the connective layer that turns isolated efforts into an enterprise capability. This position carries executive sponsorship and the active support of the BeyondTrust AI Steering Committee, giving you the organizational credibility to move fast and drive alignment across business units.
This is an individual contributor role today — and a team\-building opportunity as the practice matures. As AI adoption scales and budget follows, there is a clear path to building a dedicated team.
What You'll Do
- Cross\-Functional Architecture \& Integration. Design and implement end\-to\-end AI workflows across teams (e.g., RevOps, Legal, Marketing, Security, Finance, Customer Success). Connect data platforms, AI models (e.g., Claude, ChatGPT, Copilot), and internal applications into integrated, production\-grade solutions. Ensure all implementations are scalable, secure, and maintainable across global regions. Own and communicate an enterprise\-wide AI opportunity roadmap aligned to business priorities.
- Drive implementation. Provide concrete, tailored guidance on AI tools, efficient and secure usage, vendors, and architectures — from LLM integration and prompt engineering to workflow automation, agentic systems, and intelligent data pipelines.
- Champion responsible AI adoption. Ensure all AI deployments align with the company's security posture, data governance standards, and multi\-country regulatory requirements — leveraging deep cybersecurity context to address risk proactively before it reaches deployment.
- Build internal AI capability. Design and deliver enablement content — playbooks, workshops, executive briefings, and reference guides — that raise AI literacy and capability across the organization at all levels.
- Track Optimization and communicate value. Monitor and improve model usage efficiency, consumption patterns, and system performance across all AI implementations. Implement best practices including prompt optimization, caching strategies, and shared resource management. Define success metrics for AI initiatives, monitor adoption and outcomes across departments, and present ROI results and strategic recommendations to the SVP of Information Systems and executive leadership.
- Facilitate cross\-departmental knowledge sharing. Establish and lead communities of practice where teams share wins, lessons learned, and reusable AI assets — preventing duplicated effort and accelerating adoption across global regions.
- Facilitate company\-wide AI training. Design and deliver structured training programs — workshops, onboarding modules, and role\-specific enablement sessions — that build practical AI fluency across BeyondTrust. Partner with HR and departmental leaders to ensure training is embedded into workflows, not treated as a one\-time event.
- Stay ahead of the market. Monitor the evolving AI vendor and tooling landscape, represent the company at industry events, and bring emerging best practices and capabilities inward before competitors capitalize on them.
- Influence roadmaps without authority. Build senior\-level relationships across business units to shape priorities and investment decisions. This position carries executive sponsorship and operates with the backing of the AI Steering Committee — giving you the organizational credibility to drive alignment, not just advocate for it.
Key Impact Areas
- Security \& Compliance. AI\-assisted threat detection, alert triage automation, compliance monitoring acceleration, and incident response workflow optimization.
- Revenue \& Go\-to\-Market. Sales intelligence tooling, AI\-assisted proposal generation, competitive research automation, churn prediction, and account expansion signal detection. Marketing \& Demand: content generation at scale, brand voice consistency, persona/ICP synthesis, ABM intent signal aggregation (6Sense), and attribution modeling.
- Operations \& Finance. Procurement automation, financial modeling support, reporting pipelines, contract analysis, and FP\&A process acceleration.
- Customer Success. Intelligent support ticket routing, knowledge base surfacing, customer health scoring, and AI\-assisted renewal risk identification.
- People \& Corporate Functions. HR process automation, internal knowledge management, legal document review, and cross\-functional productivity tooling.
- Marketing \& Demand. Content generation at scale, brand voice consistency, persona/ICP synthesis, ABM intent signal aggregation (6Sense), and attribution modeling.
What You'll Bring
- 10–15 years of experience in technology, engineering, or architecture roles with meaningful AI/ML build exposure in the last 3–4 years
- Deep working knowledge of the modern AI tooling landscape: LLMs, agentic frameworks, RPA, vector databases, and no\-code/low\-code automation platforms
- Working knowledge of cybersecurity principles sufficient to build credibility with security\-native audiences and identify data risk in AI deployments
- Exceptional executive communication skills — able to translate technical AI concepts for a CFO and build a credible business case for an engineering leader
- Proven ability to drive enterprise change through influence, not authority, across complex multi\-stakeholder environments
- Experience operating in multi\-country organizations with diverse regulatory, cultural, and compliance contexts
- Track record of quantifying and communicating business value from technology initiatives
- Formal or informal experience in change management, organizational learning, or internal consulting
Nice To Have
- Background in SaaS, cybersecurity, or enterprise software organizations above $500M in revenue
- Experience building or advising AI Centers of Excellence or internal innovation programs
- Published thought leadership or conference speaking in enterprise AI, automation, or digital transformation
Who You Are
You are energized by being a multiplier — someone who does not need a team to have outsized impact. You are curious by default, politically astute without being political, and you genuinely believe AI can make people's work more meaningful. You are energized by ambiguity, comfortable navigating a fast\-moving environment without waiting for permission, and skilled at earning trust with both engineers and executives. You think in systems, move in iterations, and measure in outcomes. You bring technical depth, intellectual curiosity, and the kind of cross\-functional instinct that lets you see a use case in Legal and recognize the same pattern applies in RevOps. You would rather build a playbook that 300 people use than ship a project only you touch. You bring executive presence, intellectual humility, and the ability to hold ambiguity without losing momentum.
Better Together
Diversity. Inclusion. They're more than just words for us. They are the guiding values of how we build our teams, cultivate leaders, and create a culture where people feel connected.
We take care of our employees so they can take care of our customers. Customers who come from all walks of life just like us. We hire incredible people from diverse backgrounds because when we are different together, we are stronger together.
About Us
BeyondTrust is the global identity security leader protecting Paths to Privilege™. Our identity\-centric approach goes beyond securing privileges and access, empowering organizations with the most effective solution to manage the entire identity attack surface and neutralize threats, whether from external attacks or insiders.
BeyondTrust is leading the charge in transforming identity security to prevent breaches and limit the blast radius of attacks, while creating a superior customer experience and operational efficiencies. We are trusted by 20,000 customers, including 75 of the Fortune 100, and our global ecosystem of partners.
Learn more at www.beyondtrust.com.
\#LI\-BS1
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 BeyondTrust, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
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.
BeyondTrust AI Hiring
BeyondTrust has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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