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About This Role
Cyber Advisors LLC (CA), headquartered in Maple Grove, MN, is seeking an experienced AI & Data Governance Engineer to help organizations confidently embrace artificial intelligence while maintaining strong security, compliance, and governance controls. CA is a steadily growing IT managed services provider (MSP) and cybersecurity business that specializes in a very high-quality, customer-focused approach to designing, managing, and maintaining our customers' IT environment. We have invested a tremendous amount of time to develop our technology, processes, and support platform. We are now adding to our team of outstanding individuals to help in our growth. Come grow with us!
You can learn more about us at (http://www.cyberadvisors.com%3C/p%3E%3Cp%3EThis) (http://www.cyberadvisors.com%3C/p%3E%3Cp%3EThis)https://cyberadvisors.com/
This is a full-time position. Working hours are Monday-Friday. Ideally be based in the Twin Cites, MN area, but can be a fully remote role.
Position Overview
This role will work directly with mid-market organizations to assess AI readiness, develop governance frameworks, implement Microsoft Purview solutions, and guide clients through the adoption of Microsoft Copilot and other AI technologies. The ideal candidate possesses deep expertise in Microsoft Purview, Microsoft Copilot, data protection, compliance, and information governance, while also maintaining a broad understanding of emerging AI platforms such as Anthropic Claude, ChatGPT, Google Gemini, and other large language models (LLMs).
The successful candidate will be equally comfortable leading executive conversations, conducting technical workshops, developing client-facing documentation, and implementing governance and security controls that enable responsible AI adoption.
Key Responsibilities
AI Strategy & Advisory
- Lead AI readiness assessments and AI governance engagements for mid-sized organizations.
- Facilitate executive and stakeholder workshops focused on AI adoption, governance, security, and business transformation.
- Develop AI governance roadmaps, policies, standards, and operating models.
- Provide guidance on responsible AI usage, risk management, compliance, and organizational change management.
- Help organizations define approved AI use cases and establish controls for emerging AI technologies.
Microsoft Purview & Data Governance
- Design and implement Microsoft Purview solutions, including:
- Data Loss Prevention (DLP)
- Information Protection and Sensitivity Labels
- Data Security Posture Management (DSPM)
- Insider Risk Management
- Compliance Manager
- eDiscovery and Audit
- Data Lifecycle Management
- Data Classification and Discovery
- Conduct data governance assessments and assist clients in developing governance programs.
- Create data classification strategies and governance standards.
- Identify and remediate oversharing, excessive permissions, and AI-related data exposure risks.
- Establish governance practices for structured and unstructured data across Microsoft 365, Azure, and other platforms.
Microsoft Copilot & AI Governance
- Lead Microsoft 365 Copilot readiness and adoption engagements.
- Assess data security risks associated with Copilot deployment.
- Develop governance frameworks for Copilot, Copilot Studio, and AI agents.
- Design controls for prompt security, data protection, retention, auditing, and monitoring.
- Assist clients with AI policy development, user training, and AI acceptable-use programs.
- Conduct assessments of shadow AI usage and unauthorized AI services.
Client Delivery & Consulting
- Lead discovery workshops and requirements-gathering sessions.
- Develop assessment reports, implementation plans, architecture diagrams, and executive presentations.
- Serve as a trusted advisor for business and technology leadership teams.
- Deliver both strategic recommendations and technical implementation guidance.
- Collaborate with internal engineering teams to ensure successful project execution.
Required Qualifications
Technical Skills
- 3+ years of experience with Microsoft Purview.
- Strong expertise in Microsoft Information Protection, Microsoft Purview DLP, compliance and risk management, data classification and labeling, records and retention management, and eDiscovery and Audit.
- Experience implementing Microsoft 365 security and governance controls.
- Deep understanding of Microsoft 365 Copilot, Copilot governance, Copilot readiness assessments, AI risk management, and data governance frameworks.
- Familiarity with Microsoft Defender, Microsoft Entra ID, SharePoint, Teams, Exchange Online, and Microsoft 365 security architecture.
- Knowledge of regulatory and security frameworks such as GLBA, FFIEC, HIPAA, SOX, CJIS, CMMC, and NIST.
Consulting & Communication Skills
- Exceptional written and verbal communication skills.
- Strong documentation and presentation abilities.
- Experience leading executive-level and stakeholder conversations.
- Ability to translate technical concepts into business-focused outcomes.
- Experience creating governance documentation, standards, policies, and assessment reports.
- Proven ability to lead projects independently.
Preferred Qualifications
- Experience with Azure AI Services, Azure OpenAI, or Microsoft Fabric.
- Experience with AI governance platforms and frameworks.
- Familiarity with Anthropic Claude, ChatGPT, Google Gemini, DeepSeek, and other enterprise AI platforms.
- Knowledge of AI risk management frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, and Microsoft Responsible AI principles.
- Experience developing AI governance programs from the ground up.
- Prior consulting or professional services experience.
Preferred Certifications
- Microsoft Certified: Information Protection and Compliance Administrator Associate (SC-400)
- Microsoft Certified: Cybersecurity Architect Expert (SC-100)
- Microsoft Certified: Security Operations Analyst Associate (SC-200)
- Microsoft Certified: Identity and Access Administrator Associate (SC-300)
- Microsoft Certified: Azure Solutions Architect Expert (AZ-305)
- Microsoft 365 Certified: Administrator Expert
What Success Looks Like
In the first year, this individual will:
- Lead Microsoft Purview and AI governance engagements for clients.
- Become a trusted advisor for data governance and AI strategy initiatives.
- Develop repeatable service offerings and governance frameworks.
- Assist organizations in securely adopting Microsoft Copilot and other AI solutions.
- Contribute to the growth and maturity of Cyber Advisors' AI & Data Governance practice.
About Cyber Advisors:
Cyber Advisors' culture is like no other. First and absolutely foremost, we believe in taking care of our employees and their families. Whether we are blowing off steam at Top Golf, gathering for a fun summer car show, or hosting sales –vs- engineering paintball war, we play just as hard as we work. We believe good things come to those who sweat.
WHAT WE OFFER
- Competitive salary depending on skills and experience
- PTO and 8 Paid Holidays
- Employer-paid Health and Dental Insurance for CA employees
- Great opportunities for career advancement
- 401k with employer matching
- Disability and Life Insurance
Salary Context
This $110K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Cyber Advisors, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($120K) sits 43% below the category median. Disclosed range: $110K to $130K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Cyber Advisors AI Hiring
Cyber Advisors has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Maple Grove, MN, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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