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About This Role
Overview
Are you passionate about cloud and artificial intelligence (AI) applications and interested in solving complex technical challenges? We invite you to join Microsoft as a Cloud & AI Principal Solution Engineer supporting commercial customers. In this role, you will work on innovative projects that use Microsoft technologies to drive meaningful customer impact while collaborating across teams and continuing to develop your technical expertise.
Microsoft is transforming the developer experience for cloud and AI applications across the development lifecycle. Through AI Foundry and its integration with Azure AI, we are advancing agentic AI development, where AI agents help streamline development and operations. As a Cloud & AI Principal Solution Engineer, you will help customers adopt these technologies to improve productivity, drive innovation, and advance their AI capabilities. You will also help enterprise AI developers realize the potential of Microsoft’s AI-powered technology stack across the development lifecycle.
In this role, you will collaborate with engineering leaders and platform teams to accelerate adoption of AI Foundry, Azure AI, and Responsible AI through hands-on engagements, including proofs of concept, hackathons, and architecture workshops. You will guide customers through secure and scalable solution design, influence technical decisions, and help integrate AI application development into deployment workflows. You will help customers modernize applications and realize the value of Microsoft’s AI platform while developing your business acumen, technical expertise, and solution design and deployment capabilities.
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
- Drive technical sales engagements with decision-makers by using demonstrations and proofs of concept to influence solution design and enable production deployments
- Lead hands-on engagements, including hackathons, collaborative coding sessions, and architecture workshops, to accelerate adoption of Microsoft developer tools and cloud platforms
- Build trusted relationships with developers and platform leaders to co-design secure and scalable architectures and solutions
- Resolve technical blockers and address objections while collaborating with engineering teams to share customer insights and improve products
- Maintain expertise in AI Foundry and application architecture, including agentic AI frameworks, Semantic Kernel, the Foundry software development kit, Responsible AI, cloud-native development, application programming interfaces, containerization, microservices, event-driven architectures, Python, Java, and .NET
- Maintain and grow expertise in AI management and security
- Represent Microsoft through thought leadership in developer communities and customer forums + Embody our culture and values
Qualifications
Required Qualifications:
- Master's Degree in Computer Science, Information Technology, or related field AND 4+ years technical pre-sales or technical consulting experience OR Bachelor's Degree in Computer Science, Information Technology, or related field AND 6+ years technical pre-sales or technical consulting experience OR 7+ years technical pre-sales or technical consulting experience 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:
- 8+ years of technical pre-sales, technical consulting, technology delivery, or related experience OR equivalent experience
- 6+ years of experience with cloud, hybrid, or on-premises infrastructure, architecture design, migrations, industry standards, and/or technology management
- Certification in relevant technologies or disciplines, such as Office 365, Power BI, Azure architecture and development, cloud platform technologies, information security, or architecture
- Proficiency in agentic AI frameworks, AI management, and security, including generative AI operations, Microsoft Sentinel, Responsible AI, orchestration, and monitoring
- Experience with one or more development frameworks, tools, or programming languages, such as GitHub, Copilot Studio, Visual Studio Code, Jupyter, PyCharm, Node.js, Python, Java, C#, C++, or AI-assisted development tools
- Understanding of full-stack application architecture design and modernization and cloud-native development, including application programming interfaces (APIs), containerization, microservices, event-driven architectures, Python, Java, and .NET
- Familiarity with AI orchestration, including agentic AI frameworks, AI Foundry, Semantic Kernel, the Foundry software development kit (SDK), Responsible AI, fine-tuning, and inferencing
- Proven ability to lead technical engagements, such as hackathons, proofs of concept (PoCs), and minimum viable products (MVPs), that drive production-scale outcomes
Solution Engineering IC5 - 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 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($201K) sits 5% below the category median. Disclosed range: $130K to $272K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Microsoft AI Hiring
Microsoft has 84 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Research Scientist, AI Product Manager. Positions span Mountain View, CA, Singapore, Singapore, Remote, US. Compensation range: $108K - $331K.
Location Context
AI roles in Austin pay a median of $208,000 across 317 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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