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About This Role
ORCHESTRATE AI INTO REAL BUSINESS IMPACT
The purpose of the AI Orchestrator role is to translate business and engineering requirements into the implementation of AI\-driven solutions across a variety of workflows and processes. The role involves forming informed perspectives on how AI can be applied, contributing to team communication, understanding core AI concepts, and applying this knowledge in daily work. The job holder is expected to review and refine AI\-generated outputs to ensure quality and consistency, while continuously improving how AI capabilities are integrated into operational and development environments. All activities are carried out in alignment with the broader business model, technology strategy, and established guidelines for responsible and effective use of AI.
WHAT YOU'LL DO
- Actively participate in requirements engineering, including defining, analyzing, evaluating, planning, and documenting AI\-related business and technical needs.
- Document user stories, prompt requirements, technical solution approaches, and associated test cases and expected outputs.
- Support configuration, prompt\-design, workflow setup, specification development, implementation, and testing of AI\-enabled enhancements and solutions.
- Deliver practical, out\-of\-the\-box AI solutions that contribute measurable value to business workflows and processes.
- Collaborate closely with internal and external partners on technical topics related to AI tools, data flows, and integration approaches.
- Handle requests and incidents related to AI tools, outputs, workflows, and system behavior.
- Operate effectively within a large, global, multicultural organization working under agile frameworks (e.g., Scrum, Kanban).
- Actively participate in discussions on product features, AI use cases, solution design, and improvements to AI governance and operational processes.
- Support the implementation of architectural strategies for scalable, sustainable, and responsible use of AI technologies aligned with Hilti’s IT strategy.
- Coordinate with infrastructure, data, and development teams to ensure AI solutions are robust, reliable, and compliant with technical and security guidelines.
- Drive continuous improvement in how AI is embedded into operational and development activities, including automation and process optimization.
- Additional duties, as assigned.
WHAT YOU’LL BRING
- Bachelor’s degree in computer science, Business, Data Science, or a related field (or equivalent practical experience).
- Minimum two (2\) years’ experience working with digital tools, data‑driven solutions, or AI‑enabled technologies, demonstrating strong business and technical literacy.
- Practical experience applying AI tools or automation in an operational, engineering, or business environment.
- Skills in requirements engineering, including defining, analysing, evaluating, planning, and documenting AI‑related needs and workflows.
- Familiarity with version‑control tools (e.g., Git), basic scripting or programming concepts, and evaluation of AI‑generated outputs.
- Practical experience with agentic AI systems in production/near\-production, including multi\-step reasoning, tool\-use, and human\-in\-the\-loop agents.
- Familiarity with LLMs, prompt design, or AI orchestration frameworks (e.g., LangChain, LangGraph) is a strong plus.
- Hands‑on experience tin working with and implement AI tools, prompt‑engineering techniques, automation platforms, and supporting applications.
- Strong analytical, problem‑solving, and conceptual thinking skills; entrepreneurial mindset and effective team collaboration.
- Excellent written and verbal communication skills, quick learner, self‑motivated, and comfortable working in fast‑evolving technology contexts.
- Experience working in an Agile environment (e.g., Scrum, Kanban).
- Familiarity with version‑control tools (e.g., Git), basic scripting or programming concepts, and evaluation of AI‑generated outputs.
- Up to 10% international travel required.
WHAT’S IN IT FOR YOU
We're a big company with a family\-owned feel. You will be given the opportunity to make big changes that will ripple throughout the industry while feeling right at home with the Hilti family.
In addition to a competitive base salary and bonus potential, we offer a robust benefits package including a generous paid time off policy that includes vacation, personal days, health \& wellness, and 2 days per year to give back in your local community, paid family leave, educational reimbursement, 401(k) matching, medical/dental/vision coverage, and a variety of other benefits to fit the needs of our employees.
WHY HILTI
Hilti is a global leader in construction innovation, with more than 34,000 team members across 120 countries. Guided by our purpose, Making Construction Better, we’re driven to keep learning, growing, and finding new ways to make a lasting impact. Here, you’ll be empowered to use your strengths, work with a global and inclusive team, and take on meaningful challenges. At Hilti, you’ll have the chance to make your ideas, achievements, and growth real through purpose, passion, and teamwork.
COMMITMENT TO INCLUSION
At Hilti, inclusion is a key focus in how we work, lead, and grow together. We are committed to embracing diversity of thought and creating an environment that is inclusive of everyone, everywhere. We continuously strive to ensure every voice is valued and every team member feels empowered to contribute. By building on this foundation, we strengthen our teams, our innovation, and our impact, making construction better together.
Hilti Inc is committed to employing a diverse workforce. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.
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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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Hilti Group, 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 $180,000 based on 12,397 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Hilti Group AI Hiring
Hilti Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US.
Location Context
Across all AI roles, 15% (608 positions) offer remote work, while 3,392 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,102 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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.
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