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About This Role
Overview:
Company Overview
Founded in 2019, Apex Service Partners, LLC (“Apex” or the “Company”) is the industry and nationwide leader in residential home services. Apex focuses on serving the HVAC, plumbing, and electrical needs of homeowners in over forty states across the country. Apex has grown to \+100 locations with \+11,000 team members and \+$2\.5 billion in annual revenue.
The company is the largest competitor in this fragmented industry consisting of small founder\-owned businesses. The Company has a dual strategy of growing via high\-frequency acquisitions (over 225 closed by the Company to date) and aggressive organic growth initiatives, including sophisticated marketing, differentiated talent acquisition and green\-fielding new trades and locations. From the onset, the Company’s leaders have focused on building out a robust internal infrastructure to ensure scalability and better win in local markets around the country with the strongest brands and tactics. The team at “Partner Services”, the centralized support team for the Company, comprises nearly 100 hard\-working, collegial team members who are focused on the success of operations across the country. The Company recently received a new round of equity investment totaling $3\.4B and recently closed a debt refinancing in excess of $3\.0B.
Job Description – Enterprise AI Tools Administrator
Mission of the Role:
The Enterprise AI Tools Administrator will play a critical role in the future of Apex Service Partners by serving as the primary owner of the Company’s enterprise AI platform ecosystem. This individual will lead day\-to\-day administration of Apex’s AI toolset — anchored by Claude Enterprise (Anthropic), and inclusive of Microsoft Copilot, OpenAI ChatGPT Enterprise, and Google Gemini for Workspace — ensuring these platforms are deployed, governed, and continuously optimized to deliver maximum value to Apex team members across the country.
Reporting to the CIO, this role sits at the intersection of IT administration, information security, change management, and AI enablement. The Enterprise AI Tools Administrator ensures that Apex’s AI investments are operationally sound, cost\-controlled, compliant, and adopted effectively — from the front\-line technician in the field to the executive team at Partner Services.
Successful candidates must have hands\-on experience administering enterprise SaaS platforms, a curiosity and fluency with generative AI tools, and the ability to translate technical platform capabilities into practical, business\-driven outcomes in a fast\-growing, acquisition\-oriented company.
Responsibilities and Experience:
Job responsibilities include:
- Serving as the primary administrator for Apex’s Claude Enterprise console, including managing the Admin Console configuration, SSO/SAML 2\.0 and SCIM provisioning, role\-based access control (Primary Owner, Admin, and Member roles), workspace management, and seat allocation across standard and premium tiers.
- Administering Microsoft Copilot (M365\), OpenAI ChatGPT Enterprise, and Google Gemini for Workspace in parallel — managing provisioning, policy configuration, connector/plugin governance, and license optimization across all four platforms.
- Owning the Compliance API and audit log infrastructure for Claude Enterprise, enabling real\-time programmatic access to usage data and customer content for continuous monitoring, automated policy enforcement, and regulatory reporting.
- Managing organizational spend controls across all AI platforms — setting spending limits at the organization and individual user level, monitoring consumption against budget, and providing chargeback/showback reporting to department leaders.
- Configuring and maintaining data governance and security controls across all AI platforms, including IP allowlisting, session security policies, data retention configuration, zero\-data\-retention (ZDR) addenda where applicable, and integration with existing DLP and SIEM tooling.
- Maintaining an approved connector and MCP (Model Context Protocol) server registry for Claude Enterprise, routing new integration requests through IT governance processes, and configuring admin\-managed Skills and workflow packages for organization\-wide deployment.
- Tracking and reporting on AI platform adoption metrics — including active users, department penetration, feature usage, and productivity indicators — via admin dashboards and APIs, and delivering regular reporting to IT leadership and business stakeholders.
- Owning onboarding workflows and user enablement programs for all AI platforms, including coordination with HR and department leads to launch new users, build training materials, and drive adoption among field and back\-office team members.
- Managing the integration of AI platforms into the broader Apex technology stack — including coordinating with the IT Infrastructure, Enterprise Applications, and Cybersecurity teams to ensure AI tool access complies with network, identity, and security standards.
- Supporting the M\&A integration process by onboarding acquired company team members onto Apex’s AI platform stack in an aggressive yet risk\-controlled manner, in alignment with the broader IT integration playbook.
- Staying current on developments across the generative AI platform landscape, evaluating emerging capabilities and new tools, and providing informed recommendations to IT leadership on platform evolution, consolidation opportunities, and new use cases.
- Serving as the primary point of contact for AI platform vendors (Anthropic, Microsoft, OpenAI, Google), managing support escalations, participating in vendor roadmap reviews, and advocating for Apex’s feature and configuration needs.
Requisite Skills and Experiences include:
- Bachelor’s degree in Information Technology, Computer Science, Business Information Systems, or a related field required.
- 3–6\+ years of experience in enterprise SaaS administration, IT operations, or a related role, with direct hands\-on experience managing enterprise\-tier AI or productivity platforms (e.g., M365, Google Workspace, Salesforce, or equivalent). Experience in a PE\-backed or high\-growth company a plus.
- Demonstrated experience configuring SSO (SAML 2\.0 / OIDC), SCIM provisioning, and role\-based access control in an enterprise SaaS environment, ideally integrated with Okta, Azure AD / Entra ID, or Google Workspace identity providers.
- Hands\-on familiarity with one or more of the target AI platforms: Claude Enterprise (Anthropic), Microsoft Copilot (M365\), ChatGPT Enterprise (OpenAI), or Google Gemini for Workspace. Admin\-level experience with any of these platforms highly preferred.
- Experience with compliance, audit logging, and data governance in a regulated or security\-conscious enterprise environment. Familiarity with SOC 2, ISO 27001, HIPAA, or GDPR data handling requirements a plus.
- Working knowledge of REST APIs and the ability to use API\-based tooling (e.g., the Claude Compliance API or Admin API) for reporting, automation, and integration tasks. Scripting experience (Python, PowerShell, or equivalent) a plus.
- Strong analytical skills with the ability to build executive\-facing adoption and usage reports from platform dashboards and API data exports.
- Relevant certifications preferred: Microsoft Certified: M365 Administrator, Google Workspace Administrator, or equivalent AI/cloud platform certifications.
Personal Characteristics:
- Impeccable integrity and ethical standards. Completely trustworthy with access to sensitive usage data, compliance logs, and configuration controls across all enterprise AI platforms.
- High energy, bias for action, and personal accountability for results through resourcefulness that figures out how to drive action and achieve outcomes creatively with finite resources.
- Genuinely curious about generative AI — someone who actively uses these tools, follows the space closely, and brings informed enthusiasm to helping Apex team members get the most out of them.
- Comfortable rolling up sleeves across both technical administration tasks and end\-user enablement activities — equally at home configuring SCIM provisioning as presenting an adoption report to a department head.
- Client service mindset to build strong relationships with peers across IT, business operations, and the field — with a dedication to the “voice of the customer” in understanding how team members at all levels use and benefit from AI tools.
- Excellent communicator (written and oral) with a demonstrated ability to translate technical platform capabilities into plain\-language guidance for non\-technical audiences across a distributed workforce.
- Fact\-based and quantitative by nature, finding meaning in usage data and metrics to drive adoption decisions and cost management recommendations.
- Very well organized, detail\-oriented, reliable, and able to manage multiple platforms and concurrent workstreams because of stellar time management and prioritization skills.
- Works collaboratively and energetically, possessing a positive attitude (e.g. team player, sense of humor).
Other:
*Location*: Dallas, TX or Tampa, FL (Apex headquarters)
*Reports to*: Chief Information Officer (CIO)
*Office expectation: 4 days in office / 1 day remote (Wednesday)*
*Compensation*: Competitive
*Other Benefits*: medical, dental, and vision coverage, competitive PTO, sick days, and holidays, 401k matching
Posted Max Pay Rate: USD $0\.00/Yr. Posted Min Pay Rate: USD $0\.00/Yr.
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 Apex Service Partners, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Apex Service Partners AI Hiring
Apex Service Partners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irving, TX, 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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