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About This Role
Overview
Microsoft's mission is to empower every person and every organization on the planet to achieve more. Within the Customer Success Unit (CSU), the Cloud Solution Architect (CSA) team plays a central role in helping our customers accelerate Microsoft Cloud \& AI adoption — spanning Azure, Azure AI, and agentic AI — to unlock business value and solve the most complex technical challenges in the cloud.As a Cloud \& AI CSA Manager supporting our Telco, Media \& Gaming (TMG) customers, you lead a high\-performance team of cloud solution architects, combining deep technical expertise in cloud, data, and AI engineering with strong people leadership and the ability to manage multiple concurrent engagements. You are responsible for developing talent, ensuring technical excellence across deliverables, and translating the organization's Cloud \& AI strategy into concrete outcomes for TMG customers and the business.TMG customers are reinventing how the world connects, creates, and plays — telecommunications operators modernizing networks and monetizing 5G, media and entertainment companies transforming content production and distribution, and gaming leaders scaling global platforms. You lead the architects who turn Microsoft's Cloud \& AI platform into measurable value for them, while building an inclusive, high\-growth technical team.
Responsibilities People Leadership and High\-Performance Team Management
Lead, engage, and develop a team of Cloud Solution Architects, fostering a high\-performance, collaborative, and continuously growing environment.Attract, hire, and retain technical talent, with a focus on retention, career development, succession, and diversity and inclusion.Apply the Microsoft leadership model (Model – Coach – Care), leading by example, coaching the team, and genuinely caring for people.Manage the full performance cycle consistently: set clear goals, provide continuous feedback, conduct reviews, and recognize results.Foster a culture of growth mindset, continuous learning, and accountability for results.
Technical Leadership in Cloud \& AI Engineering
Apply deep knowledge of cloud, data, and AI engineering to guide architecture decisions, coding and design best practices, quality, and scalability across Azure and AI workloads.Act as the team's senior technical reference, reviewing Cloud \& AI solution architectures (Azure, Azure AI, agentic AI, data platforms) and supporting complex technical engagements with customers.Ensure engineering excellence across deliverables: development standards, automation, DevOps, MLOps, responsible AI, security, and reliability.Keep the team current on new cloud services, AI capabilities, and engineering trends, promoting ongoing technical enablement and certification.
Multi\-Project and Delivery Management
Lead multiple projects and technical engagements simultaneously, balancing priorities, deadlines, and team capacity.Plan and prioritize resources, dependencies, and risks across projects, ensuring delivery within agreed scope, timeline, and quality.Provide visibility into status, risks, and outcomes to technical and executive stakeholders through clear, concise communication.Establish processes and mechanisms that increase the team's delivery predictability and efficiency.
Customer and Business Impact
Drive Azure and AI consumption and adoption, connecting technical delivery to customers' business objectives.Build trusted relationships with customers' technical and executive stakeholders.Serve as the technical and management escalation point for critical situations.
Cross\-Functional Collaboration
Work seamlessly with sales, product engineering, partners, and other Microsoft teams (One Microsoft).Contribute to knowledge sharing and best\-practice exchange across teams and regions.
Qualifications
### Required Qualifications
- Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, Liberal Arts, or related field AND 8\+ years experience in cloud/infrastructure technologies, information technology (IT) consulting/support, systems administration, network operations, software development/support, technology solutions, practice development, architecture, and/or consulting OR equivalent experience.
- 3\+ years people management experience, including managing consultant practice managers, technical sales managers, and/or technical architect managers.
Other Requirements
This position is not eligible for visa sponsorship. Candidates must have authorization to work in the United States that does not now or in the future require employer sponsorship.
Preferred Qualifications* Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, Liberal Arts, or related field AND 12\+ years experience in cloud/infrastructure technologies, information technology (IT) consulting/support, systems administration, network operations, software development/support, technology solutions, practice development, architecture, and/or consulting OR Master's Degree in Computer Science, Information Technology, Engineering, Business, Liberal Arts, or related field AND 8\+ years experience in cloud/infrastructure technologies, technology solutions, practice development, architecture, and/or consulting OR equivalent experience.
- 6\+ years experience working in a customer\-facing role (e.g., internal and/or external).
- 6\+ years experience leading technical projects, teams, or functions.
- Technical Certification in Cloud (e.g., Azure, Amazon Web Services, Google, security certifications).
- 5\+ years people management experience, including managing consultant practice managers, technical sales managers, and/or technical architect managers.
Cloud Solution Architecture M5 \- The typical base pay range for this role across the U.S. is USD $130,900 \- $251,900 per year. 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 $165,600 \- $272,300 per year.
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 $130K-$272K range is above 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 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 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 $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 ($201K) sits 6% below the category median. Disclosed range: $130K to $272K.
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.
Microsoft AI Hiring
Microsoft has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span US, CA, US, Redmond, WA, US. Compensation range: $147K - $331K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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