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About This Role
Overview
The International Customer Success Organization (iCSU) mission is to deliver and scale world-class customer success services for Microsoft’s most strategic, innovative, complex, and globally operating customers worldwide. We help customers to accelerate business value through differentiated customer experiences that leverage Microsoft’s products and services, ignited by our people and culture. We drive cross-company alignment and execution, ensuring that we consistently exceed customers’ expectations in every interaction, whether in-product, digital, or human-centered.
We are looking for a Senior Cloud Solution Architect, Cloud & AI Business Solutions to help global customers turn their most ambitious Cloud & AI ideas into production outcomes on Azure. Whether your depth is in data platforms or in AI applications and agents, you will partner with customers to build unified, governed, AI-ready data estates and to move AI apps and agents from experimentation to production. In this customer-facing role, you will lead technical engagements across architecture design, proofs of concept, pilots, and production milestones using Azure AI Foundry, Microsoft Fabric, Azure Databricks, GitHub, and related Azure data and app services. You will use repeatable IP, Factory, Unified VAS, and Acceleration Programs to deliver scalable outcomes, generate incremental pipeline, and improve time-to-value for mission-critical workloads. This role is flexible in that you can work up to 100% from home.
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
- Engage customer IT and business leaders to understand their data, application, agent, and AI priorities; define success measures and design secure, scalable solutions that drive measurable business value and production adoption.
- Lead hands-on architecture, proofs of concept, pilots, and production deployments, including Cloud Acceleration Factory engagements, across AI-ready data platforms, analytics, databases, AI applications, and agents.
- Own end-to-end technical delivery results, ensuring completeness and accuracy of consumption and customer success plans with the Customer Success Account Manager, and drive next best actions that generate incremental Cloud pipeline.
- Guide workload security, privacy, governance, sovereignty, reliability, performance, capacity, and cost optimization, including service and model retirements, and apply responsible AI principles to production AI and agent scenarios.
- Resolve technical blockers with account teams, partners, and engineering; act as the Voice of the Customer by sharing adoption issues, capacity constraints, product gaps, and platform readiness feedback.
- Create and reuse solution accelerators, reference architectures, and repeatable IP that help customers deploy faster, and support customer and partner skilling through technical workshops and readiness activities across data platforms, AI apps, agents, and GitHub.
- Continuously build expertise in Azure AI Foundry, Copilot Studio, Agent365, GitHub and GitHub Copilot, Microsoft Fabric, Azure Databricks, Microsoft Purview, Azure databases, app platforms, containers, integration services, and DevOps; demonstrate a growth mindset by sharing knowledge and customer success stories, mentoring others, and contributing to internal and external technical communities.
- Other + Embody our culture and values
Qualifications Required Qualifications
- Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, or related field AND 4+ years experience in Cloud & AI platform technologies, including Azure architecture, AI and agents, GitHub, Agentic DevOps, AI Agent development, data platforms, application modernization, resiliency, security, and production-scale cloud delivery, information technology (IT) consulting/support, systems administration, network operations, software development/support, technology solutions, practice development, architecture, and/or consulting
- + OR equivalent experience
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
- 5+ years of technical enterprise experience in one or more of the following Cloud & AI technology areas: + Data platforms and analytics: Microsoft Fabric, Azure Databricks, Azure databases (SQL, Cosmos DB, PostgreSQL), data engineering, or Microsoft Purview governance + AI applications and agents: Azure AI Foundry, Copilot Studio, agent frameworks, LLM/RAG application development, or responsible AI + Application platforms and developer tooling: GitHub, GitHub Copilot, containers (AKS), serverless, integration services, or DevOps/Agentic DevOps
- 5+ years of experience designing and delivering secure, scalable data and AI solutions, from architecture and proof of concept through production deployment, with a focus on measurable business value and customer adoption.
- 3+ years of experience owning end-to-end technical delivery results, including resolving complex blockers and addressing security, privacy, governance, reliability, performance, cost optimization, and responsible AI considerations.
- 5+ years experience across cloud, data, AI, application, and developer platforms, with a growth mindset demonstrated through reusable solution development, technical skilling, knowledge sharing, mentoring, and engagement with technical communities
- Technical Cloud Certification in any of the following: + Azure Developer Associate (AZ-204) + Azure AI Engineer Associate (AI-102) + GitHub Copilot Certifications + Fabric Analytics Engineer Associate (DP-600) + Azure Data Scientist Associate (DP-100) + Fabric Data Engineer Associate (DP-700) + Azure Solutions Architect Expert (AZ-305)
Cloud Solution Architecture IC4 - The typical base pay range for this role across the U.S. is USD $106,400 - $203,600 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 $137,600 - $222,600 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 $106K-$222K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($164K) sits 22% below the category median. Disclosed range: $106K to $222K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Microsoft AI Hiring
Microsoft has 65 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, AI Software Engineer, AI Product Manager. Positions span TX, US, Washington, DC, US, Mountain View, CA, US. Compensation range: $178K - $331K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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