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About This Role
Job Overview
The Modern Work AI Platform Administrator is a member of Hubbell’s Modern Work Platforms team and is responsible for the administration, monitoring, and governance of the company’s AI and Agent platforms for modern work across the enterprise. Today that environment is centered on Microsoft, including Microsoft 365 Copilot, Copilot Chat, agents, and Copilot Studio, and the role ensures these platforms operate securely, reliably, and in alignment with Hubbell's governance standards. The role is intended to extend to other enterprise GenAI/LLM platforms Hubbell may adopt (Claude, ChatGPT, Grok, etc.), so the same governance and administration discipline applies regardless of vendor.
The position owns day-to-day platform configuration, policy management, consumption and usage monitoring, and data-access governance across these AI and agent platforms, starting with the Microsoft environment. As a secondary and growing focus, it provides administration and governance oversight for Copilot Studio agents, Power Platform environments, connectors, apps, and flows, along with related preview capabilities and automation scenarios. This is a platform administration and governance role. While it does not own enterprise AI strategy or lead organizational adoption, it contributes heavily to the company's AI platform strategy by partnering with those teams, surfacing usage and governance insights, recommending platform standards and guardrails, and translating emerging capabilities into practical, secure options that shape decisions and make safe adoption possible.
Candidate Profile
We care more about aptitude and trajectory than years of experience in a category that is only a few years old. The right person brings:
- Passion for AI and a track record of picking up emerging technology quickly.
- Real governance and administration depth in Microsoft 365 and Power Platform.
- A results-driven approach with a bias toward ownership.
- Willingness to own this space, form a point of view, and advocate for it.
- An admin-as-code and developer mindset.
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A Day In The Life
- Monitor the Microsoft Copilot and other AI platform roadmap and release cadence; evaluate new and preview features and assess their applicability, risk, and governance implications for Hubbell.
- Configure and manage tenant-level Copilot admin settings and policies through the Microsoft 365 admin center, including feature enablement, agent availability, and prompt and content controls.
- Extend the same administration and governance model to other enterprise AI platforms as Hubbell adopts them, so controls stay consistent across vendors.
- Track platform adoption and usage, and report trends to leadership.
- Govern data access and grounding for Copilot in partnership with Microsoft Purview owners, including sensitivity labels, DLP, oversharing remediation, and content-access boundaries for restricted SharePoint sites and repositories.
- Administer agent governance and lifecycle: agent approval workflows, access and permissions, publishing controls, monitoring, and retirement of M365 Copilot agents and SharePoint agents.
- Support Power Platform and Copilot Studio administration (early-stage and growing), including tenant and environment settings, environment routing, Managed Environments, and a zoned (citizen, partnered, professional) governance model.
- Govern connectors, data sources, and Copilot Studio data policies so agents use only approved connections, with Responsible AI guardrails and logging in place.
- Help establish and maintain platform standards, including naming conventions, environment strategy, ALM and pipeline guardrails, runbooks, and configuration documentation.
- Support risk and compliance, including audit logging, access reviews, and DLP alert triage, and remediate or retire non-compliant agents.
- Build and maintain automation using PowerShell and Microsoft Graph for provisioning, reporting, hygiene, configuration management and resource inventory.
Partner with Cybersecurity, IAM, Infrastructure, End User Services, and the Power Platform CoE to align controls, and provide maker and end-user enablement such as guardrails, documentation, and office hours, without owning the overall adoption strategy.
What will help you thrive in this role?
- 5+ years administering Microsoft 365 services at scale in an enterprise tenant, including governance and lifecycle management.
- Hands-on experience administering M365 Copilot or comparable AI and agent platforms with clear aptitude to ramp, including admin settings, usage monitoring, and agent governance.
- Hands-on Power Platform administration and Copilot Studio agent administration a plus.
- Working knowledge of Microsoft Purview data security and compliance controls (sensitivity labels, DLP, audit, retention) as applied to Copilot and generative AI.
- Solid understanding of Entra ID, RBAC, and conditional access concepts.
- Hands-on PowerShell and Microsoft Graph scripting for administration, reporting, and automation.
- Experience with Managed Environments, environment strategy, DLP and connector governance, and ALM or deployment pipelines at scale (preferred).
- Familiarity with the Power Platform CoE Starter Kit and tenant-level admin analytics (preferred).
- Exposure to Microsoft Foundry, Agent 365, and autonomous-agent governance (preferred).
- A governance-first mindset with demonstrated experience defining and enforcing tenant-level policies and standards.
- Effective cross-functional collaboration with security, identity, infrastructure, and end-user teams, with strong documentation and communication skills.
- Experience supporting a global manufacturing or industrial organization, and ITIL-aligned service management, are a plus.
Technical Competencies
- Microsoft 365 Copilot administration via the Microsoft 365 admin center and Copilot Analytics.
- Copilot Studio administration, including data policies, environment routing, channels, and Responsible AI guardrails.
- Power Platform administration via the Power Platform admin center, including Managed Environments, DLP, connectors, add-ons, credits and Dataverse capacity.
- Microsoft Purview, including Data Security Posture Management (DSPM) for AI, sensitivity labels, DLP, audit, and eDiscovery.
- PowerShell, PnP PowerShell, and Microsoft Graph scripting.
- Monitoring and telemetry across service health, usage analytics, and the CoE toolkit
Leadership Traits
- Operates with ownership and a platform stewardship mindset.
- Clear communicator who translates technical and governance detail into actionable guidance.
- Collaborative partner across Infra, EA (Enterprise Architecture), IAM (Identity and Access Management), Cybersecurity and the user community.
- Tracks the fast-moving AI and agent landscape across vendors.
- Approaches governance and lifecycle management with discipline.
Education
- Bachelor’s in Computer Science, Information Systems, Engineering, or a related field preferred.
Preferred certifications:
- Microsoft 365 Fundamentals
- Copilot and Agent Administration Fundamentals
- Microsoft Certified: Power Platform Fundamentals
- Microsoft Certified: Azure Fundamentals
The anticipated salary range for this position is $110,000 - $136,000. Final pay will be determined based on the selected candidate's experience, skills, qualifications and work location and may vary accordingly.
Hubbell offers a competitive benefits package that includes:
- medical, dental, life and disability insurance, and supplemental benefits.
- 401(k) Retirement Plan with company provided 4% automatic contribution and a Company match of 50% on employee contributions up to 6%.
- 11 employer-paid holidays, flexible vacation, 40 hours sick time, and 8 paid volunteer hours.
Hubbell Incorporated
Hubbell creates critical infrastructure solutions that power our customers, communities, people and the planet. Our company is strategically aligned around enabling grid modernization and electrification. As more products plug in to an aging grid, Hubbell solutions enable the transition to a more reliable, resilient and efficient energy infrastructure. Founded in 1888 our innovation has made us a leading global manufacturer of high quality electrical and utility solutions enabling customers to operate critical infrastructure reliably and efficiently.
The company operates in two segments. Hubbell Utility Solutions (HUS) enable the grid to conduct, communicate and control energy across utility applications and Hubbell Electrical Solutions (HES) are essential to managing power across a wide range of industries and applications. Our vertical market solutions can be applied to markets including Data Centers, Renewables, Commercial Buildings, Industrial, Telecom and Transportation. Supporting both of our business segments is our corporate and Hubbell Unified Business Solutions teams that provide consistent process, tools technologies across our businesses.
We are committed to operating sustainably and ethically while promoting an inclusive and supportive culture for our people to grow and develop in their careers. Our employees proudly work and serve our communities from our headquarters in Shelton, CT, across the United States and around the globe.
Hubbell Unified Business Solutions
Hubbell Unified Business Services (HUBS) provides corporate shared services to support all businesses across the enterprise. Functional areas include Engineering, Sales, Operations, Marketing, Finance, Legal, IT, Human Resources, Payroll, Environmental Health & Safety, and Compliance. HUBS employees are located around the world.
Hubbell Incorporated, its subsidiaries and affiliates, is an EO Employer AA: M/F/Veteran/Disability. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender identity or any other protected class.
The above summary of position responsibilities and requirements is not intended, and should not be construed, to be an exhaustive list of duties, skills, efforts, physical requirements, or working conditions associated with the position. It is intended to be an accurate reflection of those principal position elements essential for making decisions related to position performance, employee development, and compensation.
Salary Context
This $106K-$158K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Hubbell, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($132K) sits 38% below the category median. Disclosed range: $106K to $158K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Hubbell AI Hiring
Hubbell has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Shelton, CT, US. Compensation range: $158K - $158K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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