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About This Role
Overview:
What We Do
We calm the confusion of IT by guiding the connection between people and technology. If a customer is looking for a better way to manage their warehouse inventory, equip their workforce, or secure their data, we make it happen. All it takes is finding the right combination of tech hardware, software, cloud solutions, and support services. That’s what we do. We’re the IT Department’s IT Department.
Who We Are
Our team is made stronger by a multitude of backgrounds, experiences, and perspectives. It’s what makes Connection unique—what drives us to innovate and create technology solutions that stand apart from the crowd. We’d love for you to be a part of that fabric, to share your ideas and experiences with a team that thrives on fresh thinking, creativity, and helping others.
Why You Should Join Us
You’ll find supportive teammates and a rewarding career at Connection—plus great benefits. We take pride in supporting employees with a total rewards package that provides financial, emotional, and physical resources for you and your family. Our compensation, 401k plans, medical insurance, and other benefits are progressive and competitive. We value the importance of our employees’ emotional wellbeing. To support employees, we provide free therapy visits, mental health coaching and tools, and meditation resources. You’ll also enjoy a generous paid time off package that includes not only vacation and care time, but also Wellness and Volunteer Time Off days.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.
The AI Power Platform Administrator manages, supports, governs, and optimizes Microsoft Power Platform services, including Power Apps, Power Automate, Dataverse, Power BI, and Microsoft Copilot Studio. The AI Power Platform Administrator is responsible for platform administration, collaboration, environment management, security, governance, application lifecycle management (ALM), and support business users in building secure, compliant, and scalable low\-code solutions.
This role will also help business teams identify, design, and implement practical automation, analytics, and AI\-assisted workflow solutions using Microsoft Power Platform, the administrator will work closely with IT, Security, Data Governance, Enterprise Applications, and business stakeholders across multiple departments. The position serves as a key liaison between technical teams and business users to understand requirements, communicate platform standards, coordinate governance activities, and ensure solutions remain secure, compliant, scalable, and aligned to organizational goals.
Responsibilities:
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Administer Microsoft Copilot Studio Environment and agent governance.
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Manage AI agent deployments, permissions, lifecycle, and connector usage.
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Support AI governance, responsible AI practices, and compliance policies.
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Assist business users with solution promotion and agent lifecycle management.
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Administer Microsoft Power Platform environments using the Power Platform Admin Center.
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Create, configure, and manage Power Apps, Power Automate, Power Pages, Microsoft Copilot Studio Agents, and Dataverse environments.
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Manage Development, Test/UAT, and Production environments.
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Configure tenant\-wide settings and governance controls.
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Monitor platform health, usage, and adoption trends.
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Create and manage environments based on approved business requirements.
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Implement and maintain an environment strategy aligned with organizational standards.
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Configure backup, restore, and retention practices where applicable.
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Manage capacity allocation, storage utilization, and environment lifecycle processes.
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Maintain naming standards, ownership records, and environment documentation.
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Meet with business users and stakeholder groups to understand processes, pain points, data needs, and opportunities for automation or intelligence.
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Translating business needs into clear user stories, workflow maps, solution outlines, acceptance criteria, and success measures.
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Identify when a solution is best addressed through Microsoft 365 Copilot, Copilot Studio, Power Automate, Power Apps, Power BI, SharePoint, Teams, or a combination of these tools.
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Help users distinguish between personal productivity improvements, team\-level workflows, departmental solutions, and enterprise systems that require formal IT delivery.
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Implement and manage Data Loss Prevention (DLP) policies.
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Manage security roles, role\-based access control (RBAC), and least\-privilege access.
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Coordinate with Security and Compliance teams on governance requirements.
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Implement and manage service accounts and security groups.
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Perform periodic audits of access, connector usage, applications, flows, and agents.
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Promote managed and unmanaged solutions between Development, Test, and Production environments.
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Manage solution exports, imports, connection references, and environment variables.
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Configure and support Power Platform pipelines or comparable deployment processes.
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Support source control and CI/CD best practices where adopted.
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Maintain standards and documentation for deployment readiness, rollback, and release governance.
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Monitor platform performance, availability, and service health.
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Respond to incidents, service requests, and platform\-related support cases.
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Troubleshoot failed flows, connector issues, authentication failures, and environment access problems.
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Analyze recurring issues and recommend operational improvements.
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Escalate Microsoft service issues or critical defects through appropriate support channels.
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Manage and monitor standard, premium, and custom connectors.
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Support integration with Microsoft 365, SharePoint Online, Teams, Dynamics 365, Salesforce, ServiceNow, Azure services, SQL Server, REST APIs, and other enterprise systems.
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Configure, maintain, and monitor On\-Premises Data Gateway connections where applicable.
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Partner with application owners and service owners to ensure integrations meet security and operational standards.
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Review solution outcomes with business owners and recommend improvements based on usage, feedback, errors, and changing business needs.
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Track common request patterns and identify opportunities for reusable templates, shared guidance, or broader enablement programs.
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Help teams measure time savings, process consistency, reporting improvements, user adoption, and risk reduction.
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Stay current with Microsoft 365 Copilot, Copilot Studio, Power Platform, and Power BI capabilities and translate new features into practical business use cases.
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Facilitate workshops, office hours, working sessions, and small\-group coaching for business users building AI\-enabled workflows and low\-code solutions.
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Develop practical examples, templates, job aids, and guidance that help users become more self\-sufficient.
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Help business teams evaluate existing manual processes and prioritize opportunities based on impact, effort, risk, and sustainability.
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Create clear documentation that explains how solutions work, who owns them, how they are maintained, and when to request IT support.
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Build trust with technical and non\-technical users by communicating options, tradeoffs, constraints, and next steps in plain language.
Min: USD $122,500\.00/Yr. Max: USD $159,170\.00/Yr. Qualifications:
Required competencies:
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3\-5 years of relevant Microsoft Power Platform administration, Microsoft 365 administration, application support, or enterprise platform administration experience.
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Experience supporting enterprise Power Platform environments with multiple business stakeholders.
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Experience implementing governance, security, and access management standards.
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Experience managing multiple environments and application lifecycle processes.
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Power Platform Admin Center; Microsoft Copilot Studio; Microsoft CoPilot Power Apps; Power Automate; Power Pages; Dataverse; Microsoft Copilot Studio; Power BI administration awareness
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Microsoft Entra ID; security roles; RBAC; DLP policies; service accounts; Microsoft 365 administration; access reviews
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Solutions; managed/unmanaged packages; environment variables; connection references; Power Platform Pipelines; release documentation
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SharePoint Online; Teams; Azure services; SQL Server; REST APIs; ServiceNow or ITSM integrations; On\-Premises Data Gateway
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PowerShell; JSON; basic API concepts; YAML preferred; familiarity with automation governance
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Business process analysis; requirements gathering; user stories; workflow mapping; facilitation; solution documentation; adoption planning
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Workflow design; approvals; notifications; conversational intake; Power Automate logic; connector behavior; exception handling; testing and iteration
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Strong troubleshooting, analytical, and problem\-solving skills.
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Excellent written and verbal communication skills.
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Ability to create clear technical documentation, SOPs, and governance materials.
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Strong organizational skills with the ability to manage multiple priorities.
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Customer\-service mindset with the ability to support technical and non\-technical users.
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Ability to work collaboratively with Security, Infrastructure, Application, and Business teams.
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Maintains stable, secure, and well\-governed Power Platform operations.
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Reduces governance, security, and compliance risk across apps, flows, connectors, and agents.
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Improves automation adoption while maintaining appropriate controls.
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Ensures successful Dev/Test/Prod promotion and release processes.
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Provides timely, high\-quality support for incidents and service requests.
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Maintains accurate platform documentation, inventories, and administrative SOPs.
Additional preferred competencies:
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Microsoft Certified: Power Platform Fundamentals (PL\-900\)
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Microsoft Certified: Power Platform Functional Consultant Associate (PL\-200\)
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Microsoft Certified: Power Platform Developer Associate (PL\-400\)
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Microsoft Certified: Power Platform Solution Architect Expert (PL\-600\)
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Microsoft Certified: Azure Administrator Associate (AZ\-104\)
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Microsoft Certified: Security, Compliance, and Identity Fundamentals (SC\-900\)
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Microsoft Copilot Studio administration and AI agent governance.
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AI governance, responsible AI, connector governance, and compliance programs.
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Power Platform Center of Excellence (CoE) implementation or operations.
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Enterprise environment strategy and governance.
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DevOps or CI/CD adoption for Power Platform solutions.
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ServiceNow or ITSM workflow automation and integrations.
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Large\-scale Power Platform deployments across multiple departments or business units.
Salary Context
This $122K-$159K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Connection, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $122K to $159K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Connection AI Hiring
Connection has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Merrimack, NH, US. Compensation range: $159K - $179K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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