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About This Role
Job Type: FTE
Level: Senior
Department: AI Engineering
Location: Remote (US or Canada)
Rate: $200\-$225K (rates vary in Canada)
Position Overview
The Senior Technical Engagement Lead owns the technical vision and direction across a portfolio of concurrent engagements within a single strategic account, consolidating separate projects into one coordinated build motion and serving as the trusted technical partner to client leadership. It is an architect\-level seat weighted toward engagement leadership rather than day\-to\-day build, steering technical direction across work streams and keeping them coherent as the account scales.
Responsibilities* Own the technical vision, architecture, and solution standards across all engagements within the account.
- Drive the consolidation of multiple concurrent projects into a single coordinated build motion.
- Set the technical direction the engineering team builds to, and provide planning and oversight across engagements so delivery meets client needs and quality standards.
- Partner with the Program Manager on sequencing, dependencies, and burn tracking across the portfolio.
- Serve as the trusted technical partner and primary technical point of contact for client executive stakeholders.
- Between engagements, operate as a senior solution architect on active build work.
Qualifications* Proven leadership of large, multi\-pronged engagements — multi\-month, multi\-work\-stream programs at transformation, migration, or M\&A scale.
- Able to lead a growing engineering team and carry an executive client relationship at the same time; credible and personable with senior stakeholders.
- Leadership\-weighted: comfortable steering technical strategy across a portfolio rather than working heads\-down on a single solution.
Additional Knowledge \& Skills* AI solution architecture: agentic systems and multi\-agent orchestration, retrieval\-augmented generation (RAG) and semantic search at scale, prompt and context engineering, model selection and evaluation.
- Azure AI stack: Azure AI Foundry, Azure OpenAI, Azure AI Search, Copilot Studio, Power Platform, and M365 / Graph integration.
- Data architecture: embeddings and vectorization, retrieval pipelines, Microsoft Fabric, and grounding AI systems on enterprise data sources.
- Identity, security, and governance: Entra ID, RBAC, OAuth on\-behalf\-of flows and permission inheritance, MCP server governance, and allowlist / default\-deny postures.
- Enterprise integration: REST and API integration with ERP, CRM, and line\-of\-business systems; designing for security\-first sequencing and client review gates.
- LLMOps and delivery quality: evaluation harnesses, telemetry, confidence thresholds, guardrails, and production monitoring of AI systems.
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,*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 $200K-$225K range is above 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. Disclosed range: $200K to $225K.
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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