Interested in this AI/ML Engineer role at Cyclotron?
Apply Now →Skills & Technologies
About This Role
Job Type: FTE
Level: Senior
Department: AI Transformation Group
Location: Remote (US or Canada)
Rate: $160,000 to $180,000 USD (rates vary in Canada)
Position Overview
Cyclotron's AI Transformation Group is hiring a Sr. AI Governance Consultant to drive how our enterprise clients govern AI and agents across the entire Microsoft estate. This is a senior, client-facing role that is both broad and deep. You will own AI and agent governance and data governance as your primary disciplines: Copilot Studio and Agent Builder lifecycle, Agent 365, Microsoft Purview, data readiness for AI, and responsible AI controls. You will also be expected to carry real depth in Power Platform governance and in platform and workload governance across Microsoft 365 and Azure. The right person has designed a governance operating model, sold it to an AI Council or Architecture Review Board, and then configured it in the tenant. They stood in front of an estate of agents and environments and knew where to start. You will lead engagements, shape scope with our sellers, and raise the bar for the consultants around you. We treat governance not as a brake but as the thing that lets AI scale safely.
Responsibilities
AI & Agent Governance
- Lead the design and implementation of enterprise AI and agent governance frameworks: target operating model, governance decisions, policies and standards, roles and responsibilities, and a prioritized, adoptable roadmap.
- Govern the full agent lifecycle across Agent Builder, SharePoint agents, Copilot Studio, Frontier agents, and Copilot connectors: intake and registration, routing criteria (Agent Builder vs. Copilot Studio), maker access and publishing models, production promotion checkpoints, ownership change, monitoring, and retirement.
- Design Agent 365 governance: agent identity boundaries, policy, monitoring, audit, and alignment with Microsoft Defender, Purview, and Entra ID.
- Define Copilot Studio-specific security and DLP controls (event triggers, channels, knowledge sources, HTTP requests, Graph and federated connectors), along with credit consumption and capacity guardrails.
- Stand up and formalize AI Centers of Excellence: agent lifecycle and connectors, monitoring and reporting, audit and compliance roles, review cadences (for example, Agent Insights and monthly usage reviews), and escalation into existing ARB and AI Council structures.
- Map responsible AI and regulatory expectations (NIST AI RMF, ISO/IEC 42001, EU AI Act, industry-specific requirements) into practical controls, evaluation gates, and audit evidence.
Data Governance
- Lead data governance workstreams in Microsoft Purview and Fabric: catalog, lineage, classification, data quality, sensitivity labeling, and data product ownership.
- Assess data readiness for AI: data estate assessments, oversharing risk in SharePoint and OneDrive, grounding and knowledge source governance for agents, and the data controls needed before Copilot and agents go broad.
- Design Purview protections for AI (DSPM for AI, DLP, information protection, retention, audit) and coordinate them with Power Platform and Copilot Studio policy.
- Govern Dataverse and Dataverse-to-Fabric patterns so agent and app data stays classified, owned, and auditable.
Power Platform Governance
- Design environment strategy and landing zones: tiered and functional environments, environment groups, Development to Stage/UAT to Production models, naming, ownership, and provisioning standards.
- Design classic and advanced DLP and connector classification, run impact analysis against existing resources, and remediate without breaking the business.
- Own ALM and deployment governance: solution packaging, versioning, environment variables, Power Platform Pipelines and Azure DevOps approval gates, and rollback.
- Deploy and operationalize the CoE Starter Kit and Copilot Studio Starter Kit, managed environments, and security group and role models in Entra ID.
Platform & Workload Governance
- Shape Microsoft 365 collaboration governance: Teams, SharePoint, and Exchange policy, external sharing, information barriers, lifecycle, and retention.
- Advise on Azure governance for AI workloads: landing zones, Azure Policy, RBAC, tagging, cost controls, and governance of Azure OpenAI and AI Foundry deployments.
- Define application and SDLC governance for AI solutions: API Management, Logic Apps, secrets handling, code review and model release gates, prompt management, and evaluation gates in Azure DevOps and GitHub.
Client Leadership & Growth
- Serve as the governance lead on multi-pillar engagements; direct mid-level consultants and engineers and own the quality of what ships.
- Lead executive briefings, AI Opportunity Labs, environment strategy workshops, and security and DLP strategy workshops; run the room without dominating it.
- Partner with sellers and solution engineering on discovery, estimates, WBS, and SOWs, making in-scope and out-of-scope governance offerings explicit.
- Produce client deliverables that get used: governance frameworks, playbooks, runbooks, decision logs, intake forms, and policy templates.
- Contribute to Cyclotron's Governance Offering Suite: reusable IP, accelerators, and the quality bar that separates foundational from strategic work.
Qualifications* 8+ years of enterprise technology experience, including 4+ years leading governance, security, or architecture work on Microsoft platforms in multi-business-unit, regulated, or high-scrutiny environments.
- Proven depth in AI and agent governance: Copilot Studio, Agent Builder, Microsoft 365 Copilot, and Agent 365, including publishing models, agent identity, security posture, and monitoring at enterprise scale.
- Proven depth in data governance with Microsoft Purview (information protection, DLP, data map and catalog, DSPM for AI) and working knowledge of Microsoft Fabric.
- Strong hands-on Power Platform governance: environment strategy, DLP design and enforcement, managed environments, CoE Starter Kit, and ALM with Pipelines or Azure DevOps. You can navigate PPAC quickly and know what each setting actually does.
- Solid grounding in identity and security: Entra ID, least privilege, conditional access, RBAC, Defender, and auditability, and how they map onto AI, data, and Power Platform decisions.
- Working knowledge of Azure governance (landing zones, Azure Policy, RBAC, cost controls) and Microsoft 365 collaboration governance.
- A track record of both architecting and implementing: you designed the model, secured the decision, and configured it. Portfolio evidence expected.
- Executive-grade communication and emotional intelligence: you can explain governance trade-offs and risk to a CIO, CISO, or AI Council, in writing and in the room.
- 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 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:
- You have governed an agent estate at scale, including inventory, risk triage, and bringing unmanaged agents and environments into a governed model.
- You have designed Purview controls specifically to make Copilot and agents safe to scale, and can explain how you handled oversharing and knowledge source risk.
- You have stood up an AI CoE or AI Council operating cadence at a real client and can describe the intake, escalation, and political work it took to stick.
- You have informed opinions on Agent 365, Copilot Studio security, and where Microsoft's native guardrails still fall short.
- You have translated a responsible AI framework into concrete controls, evaluation gates, and audit evidence.
- You stay current: you read the release plans, try features in preview, and have a view on where Copilot, agents, and Purview are heading next.
Additional Knowledge & Skills* Azure AI depth: Azure OpenAI, AI Foundry, AI Search, content safety, and evaluation tooling, and how they integrate with Copilot Studio.
- Pro-code extensions: Azure Functions, Logic Apps, custom connectors, API Management, and GitHub Advanced Security.
- ServiceNow or other ITSM integration for agent intake and approvals.
- Adoption and change management: AI Ambassador or champion programs, Viva Insights, and the Copilot Dashboard.
- Certifications such as SC-100, SC-401, PL-600, AB-100, or AZ-305, or equivalent demonstrated experience.
- Consulting or professional services experience, pre-sales exposure, and a track record of mentoring consultants 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.
nQxjJSSuGd
Salary Context
This $160K-$180K range is below the median 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $160K to $180K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.