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About This Role
Director of AI \& Automation
About Us:
The Visterra Landscape Group platform is ranked among North America's top 20 landscape service providers. Collectively, Visterra partner companies bring more than 200 years of expert landscape maintenance, enhancement, construction, sweeping, portering and critical winter services with a reputation for excellence in client service.
Incumbent partner leaders guide day\-to\-day operations with teams that value and prioritize safety, employee wellbeing and dynamic career pathways.
Visterra's partner companies include Outdoor Pride and Riverside Services in New Hampshire and Massachusetts; Dyna\-Mist and Texas Landscape Group in Texas, Oklahoma, Louisiana, and Arkansas; Oberson's and GroundsPRO in Ohio and Kentucky; H\&M Landscaping in Northeast Ohio; Land Corps in Tennessee; FullCare serving Missouri and the broader Midwest region; and Clover, serving Alabama.
Role Overview:
The Director of AI \& Automation serves as a strategic and technical leader responsible for advancing Visterra Landscape Group’s automation, artificial intelligence, and process optimization initiatives. This role combines hands\-on technical expertise with strategic leadership to design, implement, and scale intelligent solutions that enhance operational efficiency, improve service delivery, and support business growth across the organization.
Reporting to the Vice President of Technology, the Director of AI \& Automation will partner closely with Operations, Finance, Sales, and other business leaders to identify opportunities for automation, develop scalable AI\-driven solutions, and drive enterprise\-wide adoption of emerging technologies.
Key Responsibilities:
- Develop and execute Visterra’s enterprise strategy for artificial intelligence, machine learning, workflow automation, and intelligent process improvement.
- Design, build, implement, and maintain AI\-powered solutions, intelligent agents, and automated workflows that improve operational efficiency and eliminate manual processes.
- Architect scalable solutions utilizing Microsoft Power Platform technologies, including Power Automate, Power Apps, AI Builder, and emerging generative AI tools.
- Partner with business leaders to identify, prioritize, and deliver automation initiatives that improve productivity, reduce costs, and drive operational performance.
- Translate complex business challenges into technical requirements and develop innovative solutions that deliver measurable business outcomes.
- Develop and maintain integrations between automation platforms and core business systems, including Aspire, Acumatica, and other enterprise applications.
- Collaborate with Analytics and Reporting teams to leverage data assets, predictive modeling, and machine learning capabilities to support business decision\-making.
- Design and implement predictive analytics solutions to identify trends, forecast outcomes, and provide actionable business insights.
- Establish governance frameworks, security standards, and best practices for AI and automation initiatives to ensure compliance, scalability, and data integrity.Monitor emerging technologies and industry trends to identify opportunities for innovation and competitive advantage.
- Serve as the primary advisor to executive leadership regarding AI, automation, and digital transformation strategies.
- Lead cross\-functional projects and influence stakeholders across all levels of the organization to drive adoption of automation solutions.
- Mentor technical team members and promote a culture of innovation, continuous improvement, and technology adoption throughout the organization.
- Communicate complex technical concepts and analytical findings in a clear and actionable manner for business leaders and non\-technical stakeholders.
- Support strategic technology initiatives and perform other duties as assigned.
Qualifications:
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related field required.
- Seven (7\) or more years of progressive experience in automation, artificial intelligence, machine learning, enterprise systems engineering, software development, or related technology disciplines.
- Demonstrated experience designing and implementing enterprise automation solutions, AI\-driven applications, and business process optimization initiatives.
- Extensive hands\-on experience with Microsoft Power Platform technologies, including Power Automate, Power Apps, and AI Builder, strongly preferred.
- Strong proficiency in Python, SQL, API development and integration, and REST/JSON\-based services.
- Experience integrating automation platforms with ERP, CRM, field service management, and other enterprise business systems.
- Proven ability to translate complex technical solutions into measurable business value and operational improvements.
- Experience within landscaping, field services, construction, facilities management, logistics, or other multi\-location operational environments preferred.
- Strong analytical, project management, problem\-solving, and organizational skills.
- Excellent communication and presentation skills, with the ability to effectively engage executive leadership and cross\-functional stakeholders.
- Demonstrated ability to lead organizational change and drive adoption of new technologies across diverse business functions.
- Advanced degrees, AI certifications, Microsoft certifications, or other relevant technical credentials are preferred.
- Ability to travel up to 20% as needed to support branch operations, technology implementations, training initiatives, leadership meetings, and other business\-related activities.
Benefits
Visterra offers a challenging and rewarding work environment where employees are encouraged to develop and grow as professionals. In this role, you will have the opportunity to lead an important operational function while contributing to the continued growth and success of the organization.
- Paid time off
- Health and wellness coverage
- 401(k) savings plan
- Professional development opportunities
The above description is intended to describe the general content, identify the essential functions, and set forth the requirements for the performance of this job. It is not to be construed as an exhaustive statement of duties, responsibilities, or requirements.
Visterra is an equal employment opportunity (EEO)/AA employer and strongly supports diversity in the workplace (m/f/d/v). We provide equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Salary Context
This $165K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Visterra Landscape 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($172K) sits 5% below the category median. Disclosed range: $165K to $180K.
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.
Visterra Landscape Group AI Hiring
Visterra Landscape Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rosemont, IL, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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