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Overview
At Small Medium Enterprises and Channel (SME\&C), we are leading a high\-growth, AI\-powered global sales team—one that is deeply connected to our partners and driven by customer success. By uniting our Small Medium Business, Corporate, Strategy, and Partner teams, we are unlocking the largest customer opportunity, backed by the industry’s most significant investments. Leveraging the power of AI and our extensive partner ecosystem, we are redefining how businesses of all sizes adopt technology to drive growth and innovation.
SME\&C is more than a sales organization—it’s a culture of innovation, opportunity, and inclusivity. Here, you’ll be part of a diverse, high\-performing, and customer\-obsessed team where collaboration, connection, and continuous learning fuel everything we do.
Are you passionate about AI and eager to transform the IT landscape? Join our dynamic Cloud \& AI team as we lead the charge in AI transformation! As trusted advisors, we help customers modernize their infrastructure, optimize operations, and drive innovation with Cloud \& AI's cutting\-edge AI capabilities. Whether it's migrating and modernizing estates to be AI\-ready, accelerating innovation with AI agents and platforms, empowering development teams with AI, or unifying and modernizing data estates, we help customers build technology solutions to achieve their business needs.
As a Digital Solution Area Specialist \- Cloud \& AI you'll be at the forefront of AI growth and disruption, guiding customers through their AI journey and helping them achieve their strategic goals. You'll ensure customers can fully harness the transformative potential of AI to stay ahead of the competition. If you're ready to make a significant impact and drive AI transformation, we invite you to join us and be part of this exciting journey!
As a Cloud \& AI Digital Solution Area Specialist you will be a solution sales leader within our mid\-market sales organization working with our most important customers. You will collaborate with a team of technical, partner, and consulting resources to advance the sales process and exceed quarterly Cloud \& AI revenue targets in your assigned accounts.
This opportunity will allow you to:
- Accelerate your career growth by leading high\-impact Cloud and AI Solution Plays across diverse industries and engaging in strategic customer conversations.
- Hone your technical and consultative selling skills through deep engagements with customers and partners, while building confidence in solution design and execution.
- Develop deep business acumen by aligning cloud strategies with customer transformation goals and success metrics, and by participating in immersive learning experiences.
- Strengthen your leadership and influence by collaborating across teams, contributing to a culture of innovation, and sharing best practices.
- Build future\-ready capabilities through certifications, hands\-on workshops, and continuous learning in cloud and AI technologies.
If you thrive in a fast\-paced, digital\-first environment and are eager to make a meaningful impact, explore how SME\&C can be the next step in your career. Together, we are shaping the future of business.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Responsibilities
Lead Customer Engagements: Act as the Cloud \& AI sales lead, guiding customers through cloud transformation across infrastructure, data, and AI platforms. Address business and technical needs with demos, whiteboarding, and solution storytelling.
Own and Convert Pipeline: Manage forecast and pipeline from early\-stage to close. Use tools like Industry Account Plans (IAP), Landing Experience Plans (LXP), and checklists to execute territory planning and strategies and drive revenue.
Orchestrate Across Teams: Partner with Account Executives, Solution Engineers, Cloud Solution Architects, and partners to co\-sell, validate solutions, and ensure successful deployment through the Cloud \& AI Marketplace.
Accelerate Decisions: Engage decision\-makers with clear business cases using Cloud \& AI Pricing Calculator, ROI, and TCO tools. Scope proofs of concept and lead customers from commitment to consumption.
Inspire and Influence: Bring technical fluency and inclusive leadership to virtual teams. Solve challenges with expertise in virtualization, cloud\-native tech, data modernization, and AI.
Qualifications
Required/minimum qualifications
- Bachelor's Degree in Information Technology, Business Administration, or related field AND 3\+ years of technology\-related sales or account management experience OR 4\+ years of technology\-related sales or account management experience.
Additional or preferred qualifications
- Demonstrated energy, accountability, and ownership, with a proven track record in consumption\-focused sales and driving customer adoption and usage outcomes.
- 2\+ years of solution sales or consulting services sales experience
- Background in Retail and/or Consumer Packaged Goods (CPG) industries, with experience delivering measurable business outcomes for customers within these sectors.
- Experience managing and scaling a Corporate Experience, with the ability to execute across a broad and dynamic customer base.
Digital Solution Area Specialists IC3 \- The typical base pay range for this role across the U.S. is USD $31\.15 \- $64\.95 per hour. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $48\.37 \- $69\.18 per hour.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us\-corporate\-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.
Salary Context
This $64K-$143K range is in the lower quartile 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 Microsoft, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($104K) sits 52% below the category median. Disclosed range: $64K to $143K.
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.
Microsoft AI Hiring
Microsoft has 29 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span US, Redmond, WA, US, Dallas, TX, US. Compensation range: $143K - $304K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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