Interested in this AI/ML Engineer role at Cyclotron?
Apply Now →Skills & Technologies
About This Role
Job Type: FTE
Level: Senior
Department: Sales
Location: Remote (US or Canada)
Rate: OTE $250K (rates vary in Canada)
Position Overview
As a Principal Cloud & AI Solutions Architect at Cyclotron, you will be responsible for driving sales wins through a disciplined presales methodology, strong technical credibility, exceptional discovery skills, thoughtful solution shaping, and confident executive-level communication. You will lead early-stage engagements, uncover true business drivers, and design responsible, scalable solutions across the Microsoft cloud ecosystem.
If you thrive at the intersection of AI, cloud architecture, and consulting strategy, this is the role for you.
Responsibilities* Lead 60–120 minute enterprise discovery sessions that surface true drivers, constraints, and decision dynamics.
- Challenge client assumptions respectfully and reframe feature requests into business problems + measurable outcomes.
- Architect credible solutions across Azure, Data, AI, Security, Integration, and Power Platform.
- Produce high-quality SOWs, scopes, assumptions, dependencies, and change-control language.
- Design phased delivery approaches that de-risk early (data readiness, feasibility, security constraints).
- Communicate effectively with CFO/COO audiences (value, impact, risk) and CTO/CISO audiences (architecture, feasibility).
- Shape engagements that balance margin, utilization, and delivery risk.
- Partner with sales and delivery for clean handoffs and deal readiness.
- Translate AI/Copilot/automation requests into clearly defined problems, measurable outcomes, and value hypotheses.
- Support sales, delivery, and leadership through clean handoffs and deal orchestration.
- Protect delivery teams by identifying risks, gaps, and misaligned client expectations early.
Qualifications* Experience: 8+ years in Microsoft cloud, data, or application architecture; 3–5 years in pre-sales, advisory, or solution architecture roles.
- Demonstrated experience leading enterprise discovery or design workshops.
- Certifications such as LUMA, IDEO, IBM Enterprise Design Thinking, Microsoft Catalyst, or equivalent structured problem solving frameworks.
- Prior experience at a Microsoft-focused consulting partner (e.g., Avanade, Slalom, Deloitte, EY, Accenture) strongly preferred.
- Exposure to Prosci or similar change management methodologies.
Additional Knowledge & Skills* Produces high-quality SOWs, 1–2 page discovery summaries, phased delivery plans with gates, and DevOps-ready backlogs with estimates and assumptions
- Ability to steer conversations past feature requests into true needs and drivers.
- Writes clear, enforceable scopes with realistic boundaries and measurable outcomes.
- Comfortable whiteboarding and defending designs across Azure + Data + AI + Security + Power Platform.
- Understands margin, utilization, fixed-fee vs T&M, and risk structuring.
- Clear, concise, and credible with executive stakeholders.
- Azure data platforms: Fabric, Synapse, Data Lake, Data Factory.
- Applied AI: Azure OpenAI (RAG, agents, summarization), Azure AI Search, evaluation strategies.
- Integration patterns: APIM, Event Grid, Functions, Logic Apps, Container Apps.
- Power Platform & Copilot Studio: when to use, when not to, and governance considerations.
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.
IBcVCSK7SV
Salary Context
This $200K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($225K) sits 6% above the category median. Disclosed range: $200K to $250K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Cyclotron AI Hiring
Cyclotron has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $250K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.