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About This Role
Overview
ACES CØDE is a Microsoft engineering organization focused on reducing operational demand across the Microsoft Cloud, including but not limited to the avoidable support cases, repeated troubleshooting, and manual interventions that arise when cloud products, platforms, or processes do not work as intended. Our mission is to engineer this demand out of the Microsoft Cloud by turning customer and operational signals into lasting product improvements, intelligent diagnostics, autonomous resolution systems, and AI-powered capabilities. As cloud services grow in scale and complexity, we are moving beyond simply reacting to problems after they occur. We work to understand why operational demand exists, prevent it at the source where possible, resolve it autonomously when it does occur, and continuously learn from those signals to make our products and support experiences more reliable. We look for teammates who can navigate ambiguity, reason from data and customer signals, work across technical and organizational boundaries, and turn complex problems into durable, measurable outcomes.
Within this mission, the ACES CØDE engineering team is building domain-specific agents and services that are transforming Azure customer support from reactive, human-dependent workflows toward AI-first systems capable of resolving customer issues at Azure scale. We are seeking a Principal Applied AI Engineer to serve as a hands-on technical architect and senior developer for these production agentic AI services.
You will define and implement the architecture for next-generation AI agents, including LLM orchestration, multi-agent coordination, retrieval-augmented generation (RAG), tool integration, and evaluation systems. You will write production code, lead complex technical initiatives, establish engineering standards, and guide engineers across organizational boundaries through technical influence rather than people management.
This is a deeply technical individual contributor role with broad architectural scope and direct accountability for production outcomes. You will turn ambiguous customer and business problems into scalable systems, make critical design decisions, build foundational components, and help teams ship secure and reliable AI services that handle thousands of customer cases per month.
Every day, our customers stake their business and reputation on our cloud. You can help Azure EngOps provide our customers with the world-class cloud services they need to succeed.
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* Architect and build production agentic AI systems: Design and implement scalable AI agents and services, including LLM orchestration, multi-agent workflows, RAG pipelines, tool integrations, memory and state management, and human-in-the-loop experiences.
- Lead through hands-on engineering: Write, review, and maintain production-quality C# and Python code. Build critical components, reference implementations, prototypes, and reusable platform capabilities that accelerate delivery across the engineering organization.
- Own end-to-end technical delivery: Translate ambiguous customer support scenarios into clear technical requirements and take solutions from architecture and experimentation through implementation, deployment, operation, and continuous improvement.
- Define technical architecture and engineering standards: Establish architecture patterns, API and data contracts, code quality expectations, evaluation practices, deployment strategies, and observability standards for AI services. Drive technical consistency while allowing teams to evolve their solutions.
- Advance evaluation-driven AI development: Design and implement evaluation frameworks using rubrics, golden datasets, simulation, judge models, and offline and online evaluation. Define quality thresholds and graduation criteria for moving agents from experimentation through shadow mode and into autonomous operation.
- Own production quality and reliability: Ensure AI services meet demanding standards for correctness, availability, latency, scalability, cost, and safety. Diagnose complex production issues, lead technical incident response and root cause analysis, and engineer systemic improvements.
- Design for responsible and secure AI: Embed responsible AI principles, privacy protections, prompt-injection defenses, tool authorization, data boundaries, action controls, auditability, and fail-safe behavior into system architecture and implementation.
- Make consequential technical decisions: Evaluate models, frameworks, platforms, and architectural approaches using evidence from experiments and production telemetry. Balance solution quality with reliability, maintainability, latency, cost, and operational complexity.
- Influence product and platform roadmaps: Partner with product managers, researchers, platform engineers, and Azure service teams to shape technical strategy and platform capabilities. Translate customer support patterns and operational signals into durable engineering investments.
- Drive measurable customer and business impact: Instrument systems and use data to improve automation accuracy, case volume reduction, resolution time, engineering efficiency, and customer satisfaction. Connect architectural investments to measurable outcomes.
- Provide technical leadership across teams: Lead architecture reviews, resolve complex technical disagreements, and align engineers and partner organizations around shared designs. Communicate clearly with engineering teams, senior leaders, and non-technical stakeholders.
- Raise the engineering capability of the organization: Mentor engineers in system design, software engineering, applied AI, and evaluation-driven development. Create reusable guidance and help teams develop the judgment to determine where AI agents should and should not act autonomously. 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:
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:
- 15 years of professional software engineering and enterprise architecture experience designing, building, integrating, and operating complex production software systems at enterprise scale.
- Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
- Deep hands-on software development background combined with enterprise architecture expertise, with demonstrated ability to translate business and technical requirements into scalable, resilient, and maintainable system architectures.
- Proficiency in C# and/or Python, with demonstrated ability to design and implement production-quality services and distributed systems.
- Experience with applied AI systems, including LLM-based applications, prompt engineering, RAG architectures, agent frameworks, or machine learning services.
- Experience with cloud platforms, preferably Azure, and cloud-native engineering practices including microservices, APIs, containers, CI/CD, infrastructure as code, and observability.
- Proven ability to influence technical direction and align multiple teams without direct management authority.
- Experience implementing agent security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.
- Demonstrated ability to move between strategic architecture and detailed implementation, including debugging complex interactions across models, prompts, tools, data, and distributed services.
- Proven business acumen and experience connecting technical investments to measurable customer outcomes such as case volume reduction, resolution time, automation quality, and customer satisfaction.
- Track record of mentoring senior engineers and raising engineering standards across teams without formal people management responsibilities.
- Background in customer support engineering, supportability, diagnostics, or customer experience platforms is a plus.
#engops
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $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 $142K-$304K range is above the 75th percentile 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($223K) sits 5% above the category median. Disclosed range: $142K to $304K.
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
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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