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About This Role
EIS Holding, LLC, is one of the largest providers of turnkey environmental services in the country, providing soil and groundwater remediation, asbestos and lead abatement, demolition, mold remediation, and air duct cleaning.
Position Description – Director of AI Transformation
Business Group/Dept: Corporate Headquarters/AI Operations & Analytics
FLSA: Exempt
Reports To: Chief Financial Officer
Date: September 24, 2026
Purpose of the Position
This is a newly created role. The company employs 1,200+ employees across 35+ offices in environmental and infrastructure services. The Director of AI Transformation owns the company's AI strategy and is accountable for delivering it: prioritizing the AI initiatives with real P&L impact, leading them through vendors and partners to adoption, and building the governance under which AI is used. Reporting to the CFO, the role works directly with the Executive Leadership and other business leaders across the company. The mandate is measurable business value – including but not limited to sales leads, conversion rates, revenue, gross margin, and EBITDA – not research, support, or training.
Essential Position Responsibilities
AI Strategy
- Define and own the company's AI strategy and roadmap, aligned with business goals and the annual operating plan
- Identify and prioritize AI opportunities by ROI, feasibility, and strategic impact in partnership with functional leaders, and own the business case for each
- Measure the impact of AI initiatives by business outcomes (revenue generated, cost removed, risk reduced), not by usage
Delivery & Vendor Management
- Lead AI initiatives from business case through pilot, production, and adoption
- Lead the selection of AI vendors and AI software partners and manage them to scope, budget, and schedule, jointly with IT
- Recommend when to build, buy, or combine approaches; translate business requirements into specifications vendors can build to, and challenge vendor designs and estimates
- Build the year-one AI budget for Executive and Board approval, then manage it across tooling, vendors, experimentation, and enablement
Adoption & Enablement
- Deliver the role-specific training each initiative needs for adoption, in the functions it touches, through HR, the AI champion network, and the functional owners
- Build a structured AI champion network in each function and coach the AI owners inside each function, so that adoption scales beyond the central role
- Lead adoption and change management for each initiative, with adoption measured from the first week
Governance & Risk
- Establish the AI governance structure, including an AI committee that reviews initiatives, approved tools, and standards
- In partnership with Legal, Compliance, IT, and HR, define the approved tools, data-handling rules, acceptable-use standards, and guardrails for AI, so that the company is protected without slowing adoption
- Other duties as assigned
First Twelve Months
- Two AI initiatives in production with measured business impact, and a third in pilot
- AI governance structure and acceptable-use standards established
- AI literacy program launched in the functions the first initiatives touch
- Initiatives that fail their pilot stopped or descoped on evidence
Qualifications
Required
- 5–10 years of progressive experience in consulting, transformation, or operations roles, including 2+ years leading AI deployments with real users, and has taken at least two AI initiatives from business case to production and adoption through vendors or implementation partners, with measured business outcomes
- Has driven change in a multi-site or field-based operating business, as an employee or an embedded advisor, and can describe the frontline process that changed as a result
- AI literacy: a working understanding of AI tools, large language models, and automation platforms, including context windows, retrieval-augmented generation, context engineering and advanced prompting, agents, and the current limits of the tools, sufficient to direct vendors and challenge designs
- Uses AI tools daily and has built working automations, prototypes, or analyses with AI coding tools such as Claude Code or Codex; production deployment is not required
- Strong executive communication, stakeholder management, and financial acumen; credible with the CFO, the CEO, and business leaders, and equally credible with the Head of IT and vendors
Preferred
- Has defined an AI or transformation strategy and prioritized a portfolio of initiatives on P&L impact
- Has established governance frameworks and guardrails for technology use with Legal and Security
- Knowledge of the environmental services, remediation, construction, or related specialty contracting industries
Key Performance Indicators (KPIs)
AI Strategy
- AI strategy and roadmap approved as part of the annual business plan, with initiatives, owners, and targets
- Realized P&L impact of AI initiatives versus business case, reported quarterly
Delivery & Vendor Management
- AI initiatives reaching production and sustained use within the timeline in their approved business case
- Vendor and software partner delivery against agreed scope, budget, and schedule
Adoption & Enablement
- Adoption of deployed initiatives by their target roles within 90 days of rollout, at the level set in the business case
- Time saved, quality improvements, and cost impact reported to leadership quarterly
Governance & Risk
- AI governance structure and acceptable-use standards in place and operating
- AI-related risks identified and managed through the company's existing risk and compliance processes
Location: Dallas metropolitan area (corporate office in Westlake, Texas); hybrid
Minimum Education: Bachelor's degree in Engineering, Computer Science, Data Science, Business, Finance, or a related field
Travel: 10% – 25%
Equal Opportunity Employer
EIS Holding, LLC is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At EIS Holdings, LLC, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
EIS Holdings, LLC AI Hiring
EIS Holdings, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Trophy Club, TX, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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