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About This Role
Overview
The Cloud Solution Architect Director – AI Business Solutions leads a team supporting U.S. Financial Services enterprise customers across banking, capital markets, and insurance. This role ensures the team acts as the voice of the customer and partner, using industry knowledge, customer insights, and demand signals to translate business priorities into solutions that drive agentic AI adoption, cowork experiences, usage, consumption, and measurable customer value. The manager coaches the team to deliver a seamless customer experience, anticipate and remove adoption blockers, and prepare complex customers for operational readiness and long-term success. The role also guides the team in identifying cross-solution growth opportunities, applying company methodologies and operational excellence standards, and leading virtual teams that bring together the right resources to accelerate outcomes at scale. In addition, the manager is accountable for employee growth and development, project execution, and performance management.
Responsibilities
Customer Centricity
- Be the Voice Of the customer: Leads team on proactively acting as the voice of the customer/partner, sharing ideas, feedback, insights, success stories and strategic/technical input with Engineering teams, Product Offerings teams and/or internal communities
- Be the Trusted Advisor: Leads team in identifying customer/partner challenges and aligning business applications or business process-oriented solutions with Microsoft’s product strategy and customer ROI, relative to competitive offerings. Coaches on building relationships with technical and business decision makers. Leads and elevates interactions with C-suite roles to address security, compliance, and risk needs, involving experts as needed.
- Deliver Customer Satisfaction: Drives team to focus on customer/partner experience through efficient delivery and ensuring a seamless and connected customer experience.
Delivers Business Impact
- Drive Consumption/Usage through Unified Support Plans: Leads team to deliver solutions using company methodologies and proven practices/patterns to support complex customer readiness and goals. Coaches team to apply change management to drive adoption, customer satisfaction and consumption/usage. Coaches team to design well architected solutions to boost retention & growth.
- Resolve blockers with urgency: Drives team to anticipate, identify, escalate and mitigate blockers to accelerate solution deployment and usage. Coaches team on applying broad business, technical, industry, and/or enterprise knowledge to architecture or support projects to meet business and information technology (IT) requirements and resolve identified constraints.
- Identify opportunities: Drives team to identify and anticipate new opportunities for Consumption, Usage & Unified expansion (especially Enhanced Solutions) at scale by communicating a clear understanding of the Microsoft value proposition for supported platforms to empower cloud success, foster and strengthen security and resiliency, and drive AI innovation. Leads team to capture opportunities in appropriate systems, processes, and tools, working collaboratively across the organization to execute on opportunities aligned with Microsoft's Customer Engagement Model.
- Execute with Operational Excellence & Agility: Leads team to operate according to required operational excellence and proven practice standards throughout all sales stages/activities/tools of record.
Technical Leadership
- Focus on Continuous Upskilling & Improvement: Leads team to identify and communicate delivery gaps, linking patterns across business and tech to improve products, IP, and processes for scalable solutions. Leads team to contribute to Microsoft’s frameworks and methodologies. Leads team on developing local thought leadership and innovation.
- Engage in Communities: Leads team to drive v-teams, share strategic insights across Microsoft, and apply architectural knowledge in a competitive context. Leads industry engagement through presentations and events to elevate Microsoft’s presence. Leads team to contribute to IP, especially in emerging areas like AI, and to build scalable models. Leads team to participate in external communities, sharing learnings internally. 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: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($201K) sits 5% below the category median. Disclosed range: $130K to $272K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Microsoft AI Hiring
Microsoft has 62 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Research Scientist. Positions span New York, NY, US, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia, Redmond, WA, US. Compensation range: $128K - $331K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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