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Position:
Microsoft AI Engineer \- CCoE, NA
Job Description:
As a Microsoft AI Engineer, you will support partners, internal teams and end customers across North America with the development, integration and implementation of AI agents. You will help translate customer needs into practical solutions, contribute to proof of concepts and quick developments, and guide stakeholders on how Microsoft technologies can be used to deliver effective AI agent outcomes.
What Youll Be Doing
- Support partners, internal teams and end customers across North America with the development and integration of AI agents.
- Contribute to proof of concepts, demos and quick/simple developments that show how AI agents can address customer challenges.
- Engage in customer and partner discussions to understand business needs and help define the best implementation approach for AI agents.
- Provide guidance on Microsoft technologies, including Microsoft Copilot, Copilot Studio and related AI capabilities.
- Help partners identify relevant AI agent use cases and translate them into practical, commercially relevant solutions.
- Collaborate with internal sales, technical and vendor teams to support AI\-related opportunities and customer engagements.
- Share knowledge, best practices and learnings around emerging AI agent topics with internal teams and partners.
- Support customer\-facing conversations by explaining technical concepts in a clear and accessible way.
- Maintain awareness of developments in Microsoft AI technologies, security considerations and the broader AI agent landscape.
What Were Looking For
- Good understanding of Microsoft technologies, with strong Microsoft knowledge preferred.
- Microsoft Certified Frontier Transformation Engineer is strongly preferred. Willingness/ability to obtain certification is required.
- Experience with Microsoft Copilot, Copilot Studio or similar AI agent technologies is required.
- Some development skills, including the ability to create quick prototypes or use “vibe coding” approaches to support proof\-of\-concept work.
- Understanding of AI agent development and integration; this is not a pure developer role, but the successful candidate should be comfortable discussing and shaping AI agent solutions.
- Ability to support partners and customers in defining practical implementation approaches for AI agents.
- Customer\-facing experience is preferred, although candidates with the right attitude, strong communication skills and stakeholder engagement ability will also be considered.
- Strong communication skills and the ability to engage confidently with internal teams, partners and customers across different countries and functions.
- Additional Microsoft certifications would be a plus.
Work Arrangement
Fully Remote: Must be able to travel to an Arrow office location as requested by Arrow leadership.
Whats In It For You
At Arrow, we recognize that financial rewards and great benefits are important aspects of an ideal job. That’s why we offer competitive financial compensation, including various compensation plans and a solid benefits package.
- Medical, Dental, Vision Insurance
- 401k, With Matching Contributions
- Short\-Term/Long\-Term Disability Insurance
- Health Savings Account (HSA)/Health Reimbursement Account (HRA) Options
- Paid Time Off (including sick, holiday, vacation, etc.)
- Tuition Reimbursement
- Growth Opportunities
- And more!
\#LI\-EK1
Annual Hiring Range/Hourly Rate:
$138,900\.00 \- $200,000\.00 OTE
Actual compensation offer to candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level. The pay ratio between base pay and target incentive (if applicable) will be finalized at offer. Internal candidates who have current pay within or above the hiring range are still encouraged to apply if interested.
Location:
US\-CO\-Colorado (Remote Employees)
Remote work employees may be required to be present at the closest designated Arrow office for work\-related purposes, at the Company’s request and sole discretion.
Time Type:
Full time
Job Category:
Sales
EEO Statement:
Arrow is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, gender, age, sexual orientation, gender identity, national origin, veteran or disability status. (Arrow EEO/AAP policy)
All Arrow job postings are for existing job vacancies.We anticipate this requisition will be open for a minimum of five days, though it may be open for a longer period of time. We encourage your prompt application.
Salary Context
This $138K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Arrow Electronics, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($169K) sits 21% below the category median. Disclosed range: $138K to $200K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Arrow Electronics AI Hiring
Arrow Electronics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Englewood, CO, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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
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