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About This Role
Job Type: FTE
Level: Mid to Senior
Department: AI Transformation Group
Location: Remote (US or Canada)
Rate: $115,000 to $140,000 USD (rates vary in Canada)
Position Overview
Cyclotron's AI Transformation Group is hiring an AI & Data Governance Consultant to help enterprise clients govern AI and agents across the Microsoft estate. This is a hands-on, client-facing role with a broad remit: your center of gravity is AI and agent governance and data governance, with solid working skills in Power Platform and platform and workload governance. You will configure Copilot Studio and agent guardrails, build Purview controls that make Copilot safe to scale, run intake and lifecycle processes, and produce the frameworks and playbooks our clients operate from. You will work alongside a Sr. AI Governance Consultant on larger programs and own workstreams end to end on smaller ones. The right person has implemented governance in real tenants, is starting to design it, and wants to grow into leading engagements.
Responsibilities
AI & Agent Governance
- Implement agent lifecycle governance across Agent Builder, SharePoint agents, Copilot Studio, and Copilot connectors: intake forms and routing, maker access and publishing models, ownership change processes, monitoring, and retirement.
- Configure Copilot Studio-specific DLP and connector policy (event triggers, channels, knowledge sources, HTTP requests, Graph and federated connectors) and credit consumption caps.
- Support Agent 365 governance: agent inventory, identity, policy, and audit alongside Defender, Purview, and Entra ID.
- Build AI CoE operating artifacts: roles and responsibilities, monitoring cadences, Agent Insights reporting, and review and escalation workflows.
- Contribute to AI Opportunity Labs: AI solution and agent inventory, risk and security gap analysis, and governance maturity scoring.
Data Governance
- Configure Microsoft Purview for AI readiness: sensitivity labels, DLP, information protection, retention, audit, and DSPM for AI.
- Perform data estate and oversharing assessments across SharePoint, OneDrive, and Teams, and recommend remediation before Copilot and agents go broad.
- Support catalog, classification, lineage, and data ownership work in Purview and Fabric, and govern Dataverse data used by apps and agents.
Power Platform & Platform Governance
- Configure environments, environment groups, managed environments, and security group and role assignments; validate DLP impact on existing resources.
- Implement ALM processes with Power Platform Pipelines or Azure DevOps, including approval gates and end-to-end test deployments.
- Deploy and report from the CoE Starter Kit and Copilot Studio Starter Kit.
- Apply Microsoft 365 and Azure governance fundamentals where they touch AI: external sharing, retention, Azure Policy, and RBAC.
Client Engagement & Communication
- Act as a trusted advisor to client administrators, platform owners, and security teams; explain governance trade-offs clearly.
- Co-lead governance workshops, discovery sessions, and knowledge transfer; deliver instructor-led training on Copilot Studio governance when needed.
- Contribute to deliverables: governance frameworks, intake and lifecycle playbooks, runbooks, validation summaries, and decision logs.
Qualifications* 4+ years of enterprise Microsoft platform experience, with 2+ years hands-on in Copilot, Copilot Studio, Purview, or Power Platform governance or administration.
- Hands-on Copilot Studio experience, including agent publishing, security settings, knowledge sources, connectors, and how agents interact with Microsoft 365.
- Hands-on Microsoft Purview experience: sensitivity labels, DLP, retention, and audit, ideally applied to Copilot or agent scenarios.
- Working Power Platform governance experience: environment strategy, DLP, managed environments, and PPAC administration.
- Solid understanding of Entra ID, least privilege, conditional access, and RBAC as they apply to AI and data controls.
- Evidence of implementing governance in production tenants, with some exposure to designing or recommending the approach.
- Clear, confident client communication and strong emotional intelligence; you can hold your own with technical leads and brief executives with support.
- Able to work remotely within the United States or Canada with reliable overlap for Eastern and Central business hours.
Governance-fluent bar: we are looking for someone visibly engaged with how Microsoft AI, data, and automation platforms are 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:
- You have configured Copilot Studio or Agent Builder governance controls in a real tenant and can walk through the choices you made.
- You have run an oversharing or data readiness assessment ahead of a Copilot or agent rollout.
- You have supported an agent intake, approval, or CoE process that clients actually used.
- You follow the release plans for Copilot Studio, Agent 365, and Purview and try features in preview.
Additional Knowledge & Skills* Microsoft Fabric and Dataverse-to-Fabric integration patterns.
- Azure AI services: Azure OpenAI, AI Foundry, AI Search, and content safety.
- Familiarity with responsible AI frameworks such as NIST AI RMF or ISO/IEC 42001.
- ServiceNow or ITSM-based intake and approval workflows.
- Certifications such as SC-401, SC-900, PL-900, AB-100, or MS-102, or equivalent experience.
- Consulting or professional services experience.
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 $115K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($127K) sits 40% below the category median. Disclosed range: $115K to $140K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Cyclotron AI Hiring
Cyclotron has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $180K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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