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About This Role
Overview
ACES CØDE is a Microsoft engineering organization focused on reducing operational demand across the Microsoft Cloud — 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 intelligent services that are transforming Azure customer support and product quality, moving from reactive, human-only workflows toward AI-first systems that resolve customer issues and product repairs at Azure scale.
We are seeking a Senior Applied AI Engineer to design, build, and operate production agentic AI services that power AI-first support at Azure scale. The ideal candidate brings Azure product knowledge and customer-support experience to engineering decisions—recognizing recurring customer issues, investigating root causes, and determining whether diagnostic capabilities, clearer guidance, or AI agents will deliver the greatest customer benefit. Working across product, support, and engineering teams, you will help prioritize what to build next, translate customer and business problems into scalable technical solutions, and deliver direct, measurable impact for Azure customers. You will write production-quality code, own significant service components, lead technical execution within projects, and help other engineers adopt sound software and applied AI engineering practices.
Every day, our customers stake their businesses and reputations on our cloud. You can help Azure EngOps provide 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
'• Analyze data signals and uncover patterns: analyze support-case trends, customer issue patterns, and authorized technical signals at scale to uncover recurring failure patterns and gaps in Azure diagnostics and documentation.
- Design and build production AI services: Develop scalable agentic AI solutions for Azure diagnostics and documentation using LLM orchestration, RAG pipelines, agent workflows, tool integrations, memory and state management, and human-in-the-loop experiences,
- Lead through hands-on engineering: Write, review, test, and maintain production-quality C# and Python code. Build reusable components, reference implementations, and engineering patterns that accelerate team delivery.
- Own end-to-end technical delivery: Translate customer support scenarios and product requirements into technical designs, then take solutions through implementation, testing, deployment, monitoring, and continuous improvement.
- Build evaluation-driven AI systems: Develop evaluation frameworks using rubrics, golden datasets, simulations, judge models, and offline and online evaluation. Use measurable quality thresholds to guide agents from experimentation toward production operation.
- Engineer for production quality: Ensure services meet requirements for correctness, reliability, availability, latency, scalability, maintainability, cost, and safety.
- Implement secure and responsible AI patterns: Apply privacy protections, prompt-injection defenses, least-privilege tool access, identity and authorization controls, data boundaries, approval gates, auditability, and fail-safe behavior.
- Integrate with Azure services and data sources: Ground agent responses and actions in trusted, current signals by integrating with Azure data sources, APIs, and cloud-native services.
- Operate services in production: Implement CI/CD, automated testing, feature flags, staged rollouts, tracing, logging, metrics, and alerting. Diagnose production issues, participate in incident response, and drive corrective improvements.
- Make evidence-based technical decisions: Evaluate models, frameworks, prompts, retrieval approaches, and architectural options using experiments and production telemetry. Balance quality with reliability, latency, cost, maintainability, and operational complexity.
- Drive measurable customer impact: Instrument solutions and use data to improve automation quality, case-volume reduction, resolution time, engineering efficiency, and customer satisfaction.
- Provide technical leadership within projects: Lead design reviews, resolve technical challenges, coordinate implementation across contributors, and align partner teams around shared interfaces and delivery goals.
- Mentor and raise engineering capability: Support other engineers through code reviews, technical guidance, reusable documentation, and practical coaching in applied AI and production software engineering.
- Collaborate across organizational boundaries: Partner with product managers, researchers, platform engineers, Azure service teams, and support-domain experts to deliver durable solutions and shape future platform capabilities. Qualifications
Required Qualifications:
Bachelor's Degree in Computer Science or related technical field AND 4+ 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
- Experience building or integrating with LLM-based systems (prompt engineering, RAG, or agent frameworks).
- Familiarity with Azure services (Azure OpenAI, Azure AI Foundry, Azure Functions, Cosmos DB, or similar cloud-native technologies).
- Experience with CI/CD pipelines, automated testing, and production observability
- Experience with Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, or comparable enterprise AI platforms.
- Experience designing AI evaluation systems using rubrics, golden datasets, judge models, simulation, adversarial testing, and offline or online evaluation.
- Experience with RAG systems, including indexing, retrieval, ranking, grounding, citation, knowledge freshness, and access control.
- Experience implementing agent-security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.
- Experience building reliable distributed services with strong standards for scalability, resiliency, performance, observability, and cost efficiency.
- Experience with infrastructure as code, container orchestration, feature management, and staged production deployment.
- Demonstrated ability to lead technical execution, influence design decisions, and mentor engineers without formal people-management authority.
- Experience collaborating with product, platform, research, and partner engineering teams on complex co-development initiatives.
- Strong business judgment and experience connecting engineering investments to customer and operational outcomes.
- Background in customer support engineering, supportability, diagnostics, or customer-experience platforms is a plus.
#EngOps #EngOpsACES
Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $160,200 - $261,000 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 $119K-$261K 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $119K to $261K.
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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