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About This Role
Context \& Impact
For 21 years, Lansweeper has been a fast\-moving company that is not afraid to reinvent itself. We've done so multiple times as the market, our product, and our customers have evolved. Now we're facing the biggest shift yet: the AI era. We're already well into it. Engineering is on the cutting edge of AI\-assisted code development and QA, and product teams are widely using Claude Cowork, Atlassian Rovo, and other AI tools in their daily work. What's missing is the connective layer. Individual teams are adopting AI tools, but we lack the unified product workflows and tooling to turn that adoption into compounding, organization\-wide returns. This role sits at the center of Product at Lansweeper and exists to build that layer: designing, configuring, and maintaining the product workflows, use cases, and product\-owned tooling that sit on top of Lansweeper's shared company\-wide AI and data foundation.
Challenge
- Greenfield mandate. No predecessor, no inherited toolkit, no established playbook. You define the discipline as you build it.
- Structural change, not evangelization. Lansweeper's teams are already bought in on AI. The challenge is driving rapid, coordinated change across product management, product marketing, UX, enablement, and the partner ecosystem to achieve higher economies of scale and prevent drift between functions.
- Two collaboration fronts. Engineering is the most advanced area and has the highest immediate need for process integration. At the same time, the interface between Product and the go\-to\-market organization needs to be sharper and more automated. Both require close partnership.
- Complex toolchain orchestration. Connecting agentic workflows across product planning, engineering, analytics, design, and communication platforms into reliable end\-to\-end systems.
- Defining success metrics in a discipline where industry benchmarks don't yet exist.
Key Responsibilities
- Audit and map the product organization's toolchain, workflows, and friction points across product management, product marketing, UX, enablement, and the partner ecosystem. Prioritize high\-impact opportunities for AI\-native automation.
- Design and build AI\-powered workflows using orchestration platforms (n8n, Make, or equivalent), API integrations, and AI agents that replace manual coordination, reporting, and documentation. Own the full lifecycle from prototype to production.
- Work closely with Engineering to integrate and streamline the product\-engineering interface: planning handoffs, sprint coordination, release management, QA feedback loops, and cross\-functional reporting. This is where the most advanced adoption exists and the immediate need is highest.
- Build AI\-native workflows for the interface between Product and the GTM organization, ensuring product context, competitive intelligence, and launch information flow cleanly across the boundary.
- Own and configure product\-owned tools (e.g. Enterpret) and maintain a centralized product knowledge layer that makes context such as strategy, OKRs, architecture, personas, and competitive intelligence retrievable by AI agents and team members alike.
- Connect the product toolchain into automated workflows via APIs and MCP (Model Context Protocol), linking product planning, project management, analytics, design, and communication tools into a connected operating layer.
- Build and iterate on AI agents for product operations tasks: intake triage, PRD generation, status reporting, stakeholder perspective simulation, competitive analysis, and meeting preparation.
- Enable the product organization on AI\-native workflows by designing onboarding, running workshops, creating guardrails and documentation, and building fluency across all product functions.
- Measure and report on operational impact (hours saved, cycle time, decision quality, adoption rates) and build dashboards that make the value of AI\-native operations visible.
- Collaborate with Lansweeper's Operations and IT team to ensure product workflows and tooling are built on top of the shared company\-wide AI and data foundation, aligning on security, governance, and enterprise\-wide standards.
- Monitor the evolving AI tooling landscape, evaluate new platforms and models, and ensure Lansweeper's product operations infrastructure stays at the frontier.
Are you our new Head of AI\-Native Product Operations?
I am...
- A hands\-on builder. If something needs connecting, automating, or configuring, I do it myself. I get into the tools, build the workflows, and make things work.
- Technically fluent. I'm comfortable with APIs, orchestration platforms, prompt engineering, vector databases, and connecting systems. Whether my background is in engineering, product, or operations, I bring the ability to build and ship working systems.
- A systems thinker who sees how product management, UX, product marketing, enablement, and partner ecosystems connect, and how operational infrastructure can make those connections seamless.
- Equally comfortable having a technical conversation about MCP integrations with Engineering, and a strategic conversation about product operating models with the CPO.
- Someone who thrives in ambiguity. This is a greenfield role and I'm energized by that. I can assess the landscape, identify priorities, and start building from day one.
- Passionate about enabling others. I build systems that make the people around me more effective, not systems that make me indispensable. ｿ I have…
- 5\+ years in product operations, technical program management, solutions architecture, or a similar role in B2B SaaS.
- A demonstrable track record of building AI\-powered workflows or automation systems in a professional context. Not just using AI tools, but designing, connecting, and maintaining multi\-step automated workflows.
- Hands\-on experience with AI agent building, orchestration platforms (n8n, Make, Zapier), API integrations, MCP, vector databases, or similar technical infrastructure.
- Deep familiarity with the product organization toolchain: Jira, Confluence, Pendo, Enterpret, or similar platforms.
- Strong understanding of the B2B SaaS product development lifecycle across discovery, design, development, delivery, and go\-to\-market.
- Experience working across both Engineering and GTM functions on process integration, tooling, and cross functional workflows.
- A portfolio, GitHub repository, or other evidence of systems you've built. We value demonstrated building ability over certifications.
- Excellent English communication skills (CEFR C1\+). You can write documentation, present to leadership, and facilitate workshops with equal confidence. Team Info ｿ You'll report directly to the Chief Product Officer and work across the entire product organization, collaborating daily with product managers, UX designers, product marketers, enablement specialists, and the partner ecosystem team. Engineering and the broader GTM organization are your primary collaboration partners, with Engineering being the most immediate priority. Your primary infrastructure partner is Lansweeper's internal Operations and IT team. Ready to build the future of how a product organization operates? 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Lansweeper AI Hiring
Lansweeper has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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