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About This Role
Overview
Microsoft Dynamics 365 powers mission\-critical business operations across the globe. Within this ecosystem, the Customer Experience Applications (CX Apps) team delivers Dynamics 365 Sales and Service an AI\-native solution enabling organizations to build intelligent, scalable, and omnichannel customer service operations through voice, chat, SMS, and more.
As a Principal Applied AI Engineer, you will be a principal technical leader responsible for driving the architecture, design, and implementation of AI\-first experiences across the Dynamics 365 Sales and Service platform. This role blends deep AI/ML expertise with modern software engineering excellence, applied at scale to mission\-critical enterprise SaaS applications.
You will work across boundaries partnering with engineering, product, design, data science, and infrastructure teams to deliver intelligent, secure, and customer\-centric solutions. You will influence strategic decisions, guide junior engineers, and contribute directly to production systems used by some of the world’s largest enterprises.
We are looking for a results\-driven, hands\-on technical leader who thrives in fast\-paced environments, thinks end\-to\-end, and brings both a systems mindset and deep AI/ML experience to solve enterprise\-grade challenges.
Why Join Us
This is an opportunity to be part of a high\-impact, high\-visibility product group at Microsoft. You’ll help define how AI reshapes enterprise customer service experiences and build intelligent systems that empower agents, improve customer satisfaction, and transform businesses worldwide.
If you’re excited about solving real\-world problems with AI, working in a fast\-moving, collaborative engineering team, and building durable, customer\-centric solutions at scale—we’d love to hear from you.
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
As a Principal Applied AI Engineer, you will:
- Own architecture, strategy, and execution of AI\-powered features across Dynamics 365 Sales and Service, ensuring technical alignment with Microsoft’s cloud\-scale services and AI platform direction.
- Design and deliver production\-ready AI solutions that leverage large language models (LLMs), natural language understanding, speech, and real\-time reasoning to improve agent productivity and customer satisfaction.
- Lead complex technical initiatives, including AI model integration, platform scalability, reliability, and long\-term maintainability.
- Collaborate deeply with applied scientists, product managers, and UX teams to translate customer needs into intelligent capabilities that deliver measurable business value.
- Drive engineering rigor across the team by establishing high standards for code quality, observability, testing, MLOps, and secure deployment practices.
- Mentor engineers across levels, fostering a culture of innovation, inclusivity, and continuous learning.
- Proactively identify technology gaps, evaluate emerging AI frameworks/tools (including open\-source and Azure AI offerings), and champion adoption where appropriate.
- Act as a technical advisor across the broader organization, contributing to cross\-team initiatives and long\-term architectural planning.
Qualifications Required Qualifications:
- Bachelor's Degree in Computer Science or related technical field AND 6\+ years technical engineering experience with coding in languages including, but not limited to, C, C\+\+, C\#, Java, JavaScript, or Python.
+ OR equivalent experience.
Other Requirements: Ability 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:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Qualifications:
- 8\+ years of hands\-on software engineering experience with deep expertise in languages such as C\#, Python, Java, or equivalent.
- 2\+ years of experience delivering AI/ML\-based systems at production scale, ideally including LLMs, transformers, RAG pipelines, or similar architectures.
- Demonstrated experience leading engineering teams or cross\-functional initiatives involving AI systems in cloud environments.
- Proven track record of designing and deploying AI\-first applications at scale, with deep understanding of performance, privacy, compliance, and operational constraints in enterprise SaaS.
- Expertise in MLOps/LLMOps, including model versioning, retraining pipelines, A/B testing, monitoring, and rollout strategies.
- Deep knowledge of cloud platforms (preferably Azure) and experience deploying containerized AI services using Kubernetes, Docker, or similar.
- Hands\-on experience integrating models from Azure AI, OpenAI, HuggingFace, or custom\-trained models into scalable application pipelines.
- Solid architectural and systems thinking—capable of making trade\-offs between performance, cost, simplicity, and maintainability.
- Exceptional written and verbal communication skills with the ability to influence across roles and levels.
- Experience working in highly regulated or secure environments, including Zero Trust, privacy, and compliance practices.
- Prior experience working with or building solutions for customer service, CRM, or enterprise productivity scenarios.
\#BICJobs
Software Engineering IC5 \- The typical base pay range for this role across the U.S. is USD $139,900 \- $274,800 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 $188,000 \- $304,200 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 $139K-$304K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 33% above the category median. Disclosed range: $139K to $304K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Microsoft AI Hiring
Microsoft has 49 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Redmond, WA, US, San Francisco, CA, US, Washington, DC, US. Compensation range: $159K - $331K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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