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About This Role
Job Type: FTE
Level: Senior
Department: AI Engineering
Location: Remote (US or Canada)
Rate: $130,000 to $155,000 USD (rates vary in Canada)
Position Overview
Cyclotron’s AI Engineering team is hiring a Sr. AI Governance Engineer to own how our enterprise clients deploy, govern, and scale Microsoft AI and automation platforms. This is a hands\-on, client\-facing role where governance is the headline. Power Platform environment strategy, Copilot Studio agent lifecycle, DLP, identity, and ALM, but you also build. The right person has lived inside the Power Platform Admin Center long enough to have informed opinions about it, has stood up Copilot Studio agents inside regulated environments, and can walk a CIO through a managed environment design without losing the room. Bonus if you can drop into the pro\-code stack, Azure, Functions, custom connectors, when the maker fabric isn’t enough. We are looking for someone who treats governance not as a brake but as the thing that lets scale actually happen.
Responsibilities
Governance \& Strategy
- Design and implement Power Platform and Copilot Studio governance frameworks for enterprise clients: environment strategy and segmentation, DLP and connector policy design (classic and advanced), Copilot/agent lifecycle governance, and security, compliance, and identity alignment (Entra ID, RBAC).
- Contribute to the definition of enterprise operating models, guardrails, and standards for AI and automation platforms.
- Assess client maturity and recommend future\-state governance roadmaps aligned to business and risk goals; sequence the work so it is actually adoptable.
Technical Leadership* Advise on best practices across Power Apps, Power Automate (cloud and desktop), Dataverse, Copilot Studio agents (build, deploy, monitor, manage), ALM strategies (solutions, pipelines, DevOps integration), and monitoring, auditing, and operational support models.
- Review and validate platform configurations for scalability, security, and compliance. Catch the design choices that will not survive contact with production.
- Support solution architects and makers with governance\-aligned design decisions; be the person they pull into the room when the trade\-offs get hard.
Client Engagement \& Communication* Act as a trusted advisor to clients, confidently discussing platform strategy, governance trade\-offs, and risk implications with technical leads and executives alike.
- Lead or support governance workshops, discovery sessions, and executive briefings; run the room without dominating it.
- Translate technical concepts into clear, business\-friendly language for leadership and non\-technical stakeholders.
- Contribute to client deliverables: governance decks, decision logs, intake forms, policy templates, and pragmatic recommendations.
Qualifications* 5\+ years of enterprise experience across Microsoft platform delivery, with meaningful time spent on Power Platform governance, administration, or architecture in a multi\-environment, multi\-business\-unit context.
- Strong hands\-on experience with Microsoft Power Platform: Power Apps, Power Automate, Dataverse, and the Power Platform Admin Center (PPAC). You can navigate PPAC quickly and know what each setting actually does.
- Proven expertise in Power Platform governance: environment strategy, DLP policy design and enforcement, managed environments, solution ownership and access control models. You have shipped these at enterprise scale, not just read about them.
- Experience with Copilot Studio, including agent governance, publishing models, security considerations, and how agents interact with the broader Microsoft 365 surface.
- Solid understanding of identity, security, and compliance concepts: Entra ID, least privilege, conditional access, auditability, and how they map onto Power Platform and Copilot Studio decisions.
- Comfortable engaging directly with clients and articulating strategy, risks, and recommendations to executive audiences. Written and verbal, polished without being stiff.
- Familiarity with pro\-code extensions to the Power Platform: Azure Functions, Logic Apps, custom connectors, Azure DevOps pipelines is a nice\-to\-have, not a hard requirement, but expect it to come up in conversation.
- Able to work remotely within the United States with reliable overlap for Eastern and Central business hours.
Governance\-fluent bar: we are looking for someone visibly engaged with how the Microsoft AI and automation platforms actually get governed at enterprise scale. You don’t need to check every box below, but the majority should feel like a clear “yes,” and you should be ready to walk us through the relevant ones in a portfolio review:* Experience contributing to or standing up Center of Excellence (CoE) capabilities at real clients, not just deploying the toolkit, but operationalizing it: governance cadences, intake processes, maker enablement, and the political work to get adoption.
- You have informed opinions on managed environments versus default, when to use solution checker enforcement, how to design a sane DLP policy that does not break every flow, and where the current platform guardrails fall short.
- You have shipped Copilot Studio agents into regulated or otherwise high\-scrutiny environments and can walk through the publishing, security, and content\-moderation choices you made.
- You stay current with the platform: You read the Power Platform release plans, you try features in preview, and you have a view on where Copilot Studio, agents, and the broader Microsoft AI surface are heading next.
Additional Knowledge \& Skills* Pro\-code and Azure depth: Azure Functions, Logic Apps, custom connectors, API Management, Azure DevOps pipelines for Power Platform ALM. Not required, but a real differentiator.
- Hands\-on experience with Azure AI services: Azure OpenAI, AI Foundry, AI Search, and how they integrate with Copilot Studio and the Power Platform.
- Microsoft Fabric and Dataverse\-to\-Fabric integration patterns.
- Experience with the Power Platform CoE Starter Kit and customizations on top of it.
- Consulting or professional\-services experience, or prior work in a partner/ISV ecosystem.
- Track record of mentoring engineers, makers, or growing a practice.
About the Company
Cyclotron is a Microsoft Solutions Partner focused on the modern workplace, data, and AI. We are dedicated to empowering clients to streamline operations and achieve their business goals. The company fosters a collaborative and inclusive environment, encouraging continuous learning and professional growth for its employees. Cyclotron’s mission is to deliver reliable, forward\-thinking technology solutions that drive success for clients across various industries.
What We Can Offer
Cyclotron offers a comprehensive benefits package designed to support our employees’ well\-being and professional growth in a fully remote work environment. We provide competitive health, dental, and vision insurance, and we cover 100% of employee medical premiums. Our team members benefit from generous and flexible paid time off (PTO), retirement plan options, and ongoing training opportunities. Additionally, Cyclotron promotes work\-life balance with flexible work arrangements and robust wellness programs, creating a rewarding and supportive workplace for all.
*Cyclotron relies on diversity of culture and thought to deliver on our goals. To ensure we can do that, we seek talented, qualified employees to join our team, regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. We are proud to be an Equal Employment Opportunity/Affirmative Action Employer.*
*We also provide reasonable accommodation for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law.*
Cyclotron is an Equal Opportunity Employer. Cyclotron values diversity, equity and inclusion, and aims to practice DE\&I in all that we do.
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Salary Context
This $130K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cyclotron, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($142K) sits 35% below the category median. Disclosed range: $130K to $155K.
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.
Cyclotron AI Hiring
Cyclotron has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Remote, US. Compensation range: $150K - $225K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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