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About This Role
Overview
Are you curious, enthusiastic about infrastructure, and ready to solve complex challenges in the Frontier AI era? Join us as a Sr Solution Engineer, Cloud \& AI Infrastructure for commercial customers at Microsoft.
In this technical sales role, you will help customers design secure, scalable, resilient, and sovereign cloud architectures that support their Frontier transformation and modernization goals and guide organizations through modernization, migration, and transformation — translating technical capabilities into significant business outcomes.
You will collaborate across teams to deliver impactful solutions that enhance agility, reduce costs, and unlock value through AI\-powered infrastructure.
As a Sr Solution Engineer, Cloud \& AI Infrastructure, you will play a key role in helping customers modernize their Platform Estate, including hardware, software, applications, databases, security and networking with the full value of Microsoft’s cloud. You will work directly with technical and business stakeholders to design secure, scalable, resilient, compliant, and sovereign architectures that support business\-critical applications and Frontier AI transformation. As a trusted technical advisor, you’ll guide customers and win their technical decisions on Azure solutions and deployment.
You bring deep expertise in migrating core workloads—including Windows, Linux, and Oracle—to Azure. Beyond migration, you lead customers through modernization using the 6R strategy, containerization best practices, and Azure\-native services like AKS, while exploring how Agentic AI and Tooling (e.g., Azure Copilot, GitHub Copilot, Agentic Assessment) can accelerate Frontier transformation.
You complement this with strong networking knowledge to design hybrid and cloud\-native solutions, including virtual networks, VPNs, secure routing architectures and trusted partner solutions to ensure secure, optimized, and scalable solutions.
As you join the team, you will have the opportunity to accelerate your career growth, develop deep business acumen, hone your technical skills, and become adept at solution design and deployment. In summary, you’ll help customers modernize their existing platforms to realize the full value of Microsoft’s platform, all while enjoying flexible work opportunities.
Responsibilities
- Lead Platform Estate modernization \& Sovereign Cloud customer engagements.
- Drive technical workshop using live technical demos, co\-create with customers whiteboarding \& architecture, rapid prototyping, hands\-on proofs of concept \& pilots to resolve technical blockers.
- Prove technical solutions, co\-create or re\-use existing architecture to influence solution design and enable customer commitment for deployments.
- Maintain deep expertise in cloud migration (Windows, Linux, app workloads), resiliency, security, and compliance.
- Know main market enterprise apps: SAP, Oracle and VMware.
Qualifications
Required Qualifications:
- Master's Degree in Computer Science, Information Technology, or related field AND 3\+ years technical pre\-sales or technical consulting experience OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 4\+ years technical pre\-sales or technical consulting experience OR 5\+ years technical pre\-sales or technical consulting experience OR equivalent experience.
Other Requirements:
Security Clearance Requirements: Candidates must be able to meet Microsoft, customer and/or
government security screening requirements are required for this role. These requirements include,
but are not limited to the following specialized security screenings:
- The successful candidate must have an active U.S. Government Top Secret Security Clearance.
Ability to meet Microsoft, customer and/or government security screening requirements are
required for this role. Failure to maintain or obtain the appropriate clearance and/or customer
screening requirements may result in employment action up to and including termination.
- Clearance Verification: This position requires successful verification of the stated security
clearance to meet federal government customer requirements. You will be asked to provide
clearance verification information prior to an offer of employment.
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud
background check upon hire/transfer and every two years thereafter.
- Citizenship \& Citizenship Verification: This position requires verification of U.S. citizenship due
to citizenship\-based legal restrictions. Specifically, this position supports United States federal,
state, and/or local United States government agency customer and is subject to certain
citizenship\-based restrictions where required or permitted by applicable law. To meet this legal
requirement, citizenship will be verified via a valid passport, or other approved documents, or
verified US government Clearance
Preferred Qualifications:
- Proven experience in cloud infrastructure, including migration of workloads such as Windows, Linux, VM.
- Experience designing or selling solutions involving hybrid networking, secure connectivity, or network performance optimization in enterprise environments.
- Hands\-on experience with GitHub Copilot (GHCP), Agentic tooling, Windows Server, Linux, SQL Server, PostgreSQL, Azure Kubernetes Service (AKS), Azure Container Apps, and Defender for Cloud.
- Strong understanding of Azure services, including networking, security, compliance, and hybrid cloud scenarios.
- Proven ability to lead technical engagements (e.g., prototyping, pilot, PoCs) that drive production\-scale outcomes.
- 6\+ years technical pre\-sales, technical consulting, or technology delivery, or related experience
- 4\+ years experience with cloud and hybrid, or on premises infrastructure, architecture designs, migrations, industry standards, and/or technology management
- Familiarity with enterprise platforms such as SAP, Oracle, and ISVs like NetApp, VMware, and RedHat.
- Knowledge of regulatory frameworks such as GDPR, HIPAA, and Public Sector compliance standards.
Solution Engineering 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 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($164K) sits 23% below the category median. Disclosed range: $106K to $222K.
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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