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Head of AI Transformation
Lansweeper has spent 21 years adapting to market shifts. The AI era is the largest shift yet — and we are ahead of most. AI adoption iscomplete. The vast majority of our employees actively use AI tools. Engineering runs an AI\-native development lifecycle. Early automation has delivered material savings.
Now we enter the next phase in our journey to become an AI\-native organization: AI industrialization.
Our 5\-year plan requires significant revenue expansion on modest headcount growth. That only works if AI becomes embedded in our operating model — across workflows, governance, decision\-making, measurement, and enterprise infrastructure.
As Head of AI Transformation you build and lead our company\-wide AI operating model in close collaboration with the departmental AI Ops leaders. You drive AI strategy execution, AI governance, AI measurement, and AI\-native transformation at scale.
This is a newly created, global leadership role at the center of our AI transformation.
Challenge:
The main challenges you'll face are:
- Turning high AI adoption into measurable operating leverage (output per person, cycle time, cost\-to\-serve).
- Designing a scalable AI governance framework (EU AI Act aligned) without slowing innovation.
- Leading cross\-functional AI transformation without direct authority over departmental AI Ops leaders.
Key Responsibilities:
AI Strategy \& AI Operating Model
- Co\-define and maintain the company\-wide AI strategy, aligned with ExCo, other AI leaders and embedded into departmental plans.
- Build and run the AI operating model (hub\-and\-spoke structure). You will run the hub.
- Coordinate the AI Steering Committee (Engineering, Product, GTM, HR, Finance, Legal, Security, IT).
- Prepare and consolidate the monthly AI performance update for ExCo and Board via the COO.
- Coordinate the internal network of departmental AI Ops leaders to ensure alignment, leverage, and shared standards.
AI Governance \& Responsible AI
- Instrument the AI governance operating model: decision rights, cadence, escalation paths, consolidated risk view.
- Define company\-wide requirements for approved AI tools, IP protection, data leakage prevention, shadow AI monitoring, and responsible AI usage in close collaboration with the security officer.
- Design the governance model for employee\-built AI tools (prototype\-to\-production framework).
- Maintain working knowledge of the EU AI Act and other applicable regulations; partner with Legal on compliance posture.
AI Infrastructure \& Tooling Convergence
- Orchestrate the set up and maintenance of the enterprise AI context layer and knowledge infrastructure (with IT as technical owner).
- Define the decision framework for AI tooling convergence and AI platform evaluation.
- Stand up a global AI skills\-sharing mechanism to reuse AI workflows safely across teams.
- Coordinate evaluation and recommendation of new AI models and enterprise AI platforms.
AI Measurement \& Operating Leverage
- Own the AI measurement framework connecting AI initiatives to business impact
- Make AI performance data visible and actionable for ExCo and department leaders.
- Surface blockers and decision points required to unlock the next phase of AI\-native scale.
AI Enablement (in partnership with HR)
- Partner with HR on AI training programs (Responsible AI, workflow automation, function\-specific AI fluency).
- Integrate AI capability into hiring, onboarding, and performance conversations.
- Drive enablement\-first accountability across the organization.
Key Requirements:
Hard skills:
- 10\+ years in (AI) transformation, strategy execution, operational leadership, or enterprise transformation within SaaS.
- Proven experience building an (AI) operating model, governance framework, or transformation program from scratch.
- Demonstrated hands\-on experience using LLMs, AI automation, AI workflows, or AI\-native systems.
- Experience leading large\-scale cross\-functional programs without direct authority.
- Good understanding of AI regulatory compliance in a global SaaS environment.
Soft skills:
- Executive presence — able to report at ExCo level.
- Influence\-driven leadership in complex, global organizations.
- High ambiguity tolerance with structured decision\-making capability.
Our Offer:
- Hybrid working model from our Austin (US) or Merelbeke (BE) office.
- Global, high\-impact Director role reporting directly to the COO.
- Opportunity to build and lead a newly created AI Transformation function in an AI\-native SaaS company.
- Career growth in AI strategy, governance, and AI\-native transformation.
- Flexible working hours.
- Company events and international collaboration opportunities.
About Lansweeper:
Lansweeper is the AI Cyber Asset Intelligence platform helping IT and Security teams gain full visibility, reduce cyber risk, and scale automation with confidence.
In today’s complex IT, OT, cloud, and IoT environments, fragmented asset data slows decisions and increases risk. We transform raw asset data into a continuously validated, trusted source of truth — so teams can move faster and act with certainty.
With Lansweeper, organizations can:
See – Truly complete visibility across hybrid environments
Know – Enriched asset intelligence with lifecycle and risk context
Act – Automate workflows, coordinate remediation, and enforce policy at scale
From universal asset discovery to AI\-powered intelligence, we provide the shared foundation modern IT Operations, Cybersecurity, and Digital Transformation teams rely on.
Our culture:
We’re built on four core values:
One Team – United across boundaries
We Care – Customers and people at the center
We Grow – Learning, sharing, improving
We Deliver – Focusing on what truly matters
Team Info:
You’ll join the Operations team, reporting directly to the Chief Operating Officer.
You have one direct report: an AI Enablement Specialist (training, adoption tracking, capability building).
You work closely with:
- Global Senior IT Manager (AI infrastructure, security, tooling implementation)
- Head of AI\-Native Product Operations (product\-side AI workflows and agents)
- VP of Engineering
- Legal Director
- Security Officer
You indirectly coordinate AI Ops leaders embedded in Engineering, Product, GTM, Marketing, Customer Success, HR, Finance, IT, and Operations.
This is a global role with cross\-regional collaboration.
Call to Action:
Ready to lead enterprise AI transformation and build a scalable AI operating model?
Click Apply now or share this role with someone in your network.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Lansweeper, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Lansweeper AI Hiring
Lansweeper has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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