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The Director, Enterprise AI Enablement, owns the Enterprise AI Platform \& Enablement lane of QTS’ AI operating model — making AI usable, governed, and scalable for every employee across the U.S. and Europe. As a senior leader within the AI Strategy \& Implementation organization, this role delivers the Enterprise Productivity pillar of the “AI at QTS” strategy: approved tools, role\-based training, prompt libraries, and adoption for every employee. QTS is powered by people, and this role exists to make AI powerful for them — turning sanctioned tools like Microsoft 365 Copilot and the applications built by the Business AI Delivery team into confident, everyday productivity. The Director sets enterprise enablement strategy, leads the enablement organization, partners with the Business AI Delivery and every department across QTS to realize true business solutions and value. This leader also is critical to influencing the backbone of QTS’ AI Architecture and Coordination, and embeds the QTS operating principles — be creative, trust your data, and govern from day one — into how the company adopts AI.
RESPONSIBILITIES, other duties may be assigned.
Strategy \& Organizational Leadership
- Own the strategy, roadmap, and end\-to\-end delivery of the Enterprise AI Platform \& Enablement lane, making AI usable, governed, and scalable for every employee across the U.S. and Europe.
- Translate the “AI at QTS” Enterprise Productivity pillar into a multi\-year enablement plan; partner with the VP, AI Strategy \& Implementation and executive leadership to set priorities and secure investment.
- Build and lead the enablement organization — including the AI Enterprise Enablement Manager, specialists, and trainers — setting goals, developing talent, and scaling the team as adoption grows.
- Partner with the Business AI Delivery (DELIVER) and AI Governance \& Intake (GOVERN) lane leaders, on a shared backbone of AI Architecture and Coordination, to deliver one cohesive operating model.
- Direct enterprise adoption of Microsoft 365 Copilot and other approved tools — scaling from the current 1,500\+ Copilot Chat users — through role\-based training, prompt libraries, playbooks, office hours, and self\-service resources.
- Establish and grow an enterprise AI champions / community\-of\-practice network to drive grassroots adoption and surface high\-value use cases into central intake.
- Lead organizational change management for AI initiatives across the enterprise, including executive and frontline communications, resistance management, and reinforcement.
Operationalize “govern from day one” for end users: partner with AI Governance \& Intake and Enterprise Technology to advance approved\-tool standards (74 tools in use* 8 approved), the enterprise AI policy, acceptable\-use guidance, and human\-in\-the\-loop practices.
- Replace shadow AI with safe, sanctioned tools, and embed responsible\-AI guardrails into every enablement program.
- Define, track, and report enterprise adoption and value metrics — utilization, active usage, time saved, and dollar impact — and present outcomes to senior leadership.
- Own the enablement budget and forecasts; manage vendor and platform relationships (e.g., Microsoft) for licensing, enablement resources, and co\-funded adoption programs.
- Other duties as assigned.
BASIC QUALIFICATIONS
- Bachelor’s degree in a relevant field, or equivalent professional experience.
- Ten or more years of experience in technology enablement, digital adoption, learning \& development, change management, business analytics, software development or technology program management.
- Four or more years leading teams
- Demonstrated success leading enterprise\-wide adoption of a major technology, platform, or transformation program across a large or distributed organization.
- Hands\-on familiarity with modern AI tools (Microsoft 365 Copilot and/or generative\-AI assistants) and a working understanding of how large language models behave — their capabilities, limits, and appropriate use.
- Demonstrated ability to set strategy; establish governance, standards, and KPIs; and influence executives and matrixed stakeholders without direct authority.
- Excellent verbal and written communication skills, including executive\-level presentation.
PREFERRED QUALIFICATIONS
- Experience building and scaling a platform
- Familiarity with responsible\-AI / AI\-governance frameworks (e.g., NIST AI RMF) and enterprise data\-privacy and security considerations.
- Experience leading enablement across multiple geographies, including the U.S. and Europe.
- Experience in a data center, critical\-facilities, or technology\-operations environment.
We conform to all the laws, statutes, and regulations concerning equal employment opportunities and affirmative action. We strongly encourage women, minorities, individuals with disabilities and veterans to apply to all of our job openings. We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, or national origin, age, disability status, Genetic Information \& Testing, Family \& Medical Leave, protected veteran status, or any other characteristic protected by law. We prohibit retaliation against individuals who bring forth any complaint, orally or in writing, to the employer or the government, or against any individuals who assist or participate in the investigation of any complaint or discrimination claim.
The "Know Your Rights" Poster is included here:
Know Your Rights (English)
Know Your Rights (Spanish)
QTS is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e\-mail to talentacquisition@qtsdatacenters.com and let us know the nature of your request and your contact information.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At QTS, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
QTS AI Hiring
QTS has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Suwanee, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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