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About This Role
Overview
Leads team on proactively acting as the voice of the customer/partner leveraging relevant insights. Supports and coaches team on proactively identifying and/or translating customer/partner problems into industry solutions. Coaches team to focusing on customer/partner experience through efficient delivery and ensuring a seamless and connected customer experience. Drives team on anticipating, identifying, escalating, and mitigating blockers using appropriate tools. Leads team on delivering solutions in line with company methodologies to prepare complex customers for operational readiness and achievement of their business goals and targets. Drives team on proactively identifying and anticipating new cross-solution opportunities for Consumption, Usage & Unified expansion at scale. Coaches team on operating according to required operational excellence and proven practice standards throughout all sales stages/activities/tools of record. Supports and guides team on leveraging market insights and demand signals to assist leadership in identifying relevant areas. Coaches and enables team on leading, mentoring, and maybe assembling virtual teams. Leads team on proactively identifying and anticipating gaps that drive changes and improvements to scale across customers. In addition, this role has people management responsibilities including driving employee growth and development, executing projects, and managing performance.
Responsibilities
Business Impact
Coaches team on operating according to required operational excellence and proven practice standards throughout all sales stages/activities/tools of record (e.g., pipeline updates, time tracking). Leads team on orchestrating and collaborating across Microsoft and customer/partner teams through on-strategy delivery to achieve customer/partner objectives and increase customer/partner satisfaction. Oversees team on anticipating and managing business and technical risks, adapting methodology and applying governance principles to identify, communicate, and minimize business and technical risks. Executes work in compliance with industry and Microsoft guidelines and procedures. Leads team to continuously prioritize among competing demands in their work and identify where impact occurs with customers, ensuring alignment with business priorities and goals.
Leads team on delivering solutions in line with company methodologies (e.g., product offerings such as value-based deliverables [VBDs], advice, training, technical validation), ensuring proven practices and patterns are followed to prepare complex customers for operational readiness and achievement of their business goals and targets. Leveraging knowledge of change management proven practices and/or involving the change management team, proactively helps the customer deploy for long-term organizational adoption to increase customer satisfaction and drive consumption/usage. Coaches team on guiding complex customers/partners towards a well-architected (e.g., secure, resilient, artificial intelligence [AI]-enabled), and cost- and performance-optimized solution to increase retention and expansion opportunities. Provides guidance to team on articulating the value of Unified and supports sellers, partnering with account teams to build consumption plans aligned with appropriate services. Leads efforts to provide feedback to Unified Delivery Team on VBDs to refine and further develop content.
Drives team on anticipating, identifying, escalating, and mitigating blockers using appropriate tools and processes to accelerate solution deployment, value realization and usage/consumption for complex customers/scenarios. Coaches team on proactively applying broad business, technical, industry, and/or enterprise knowledge to architecture or support projects to meet business and information technology (IT) requirements and resolve identified constraints.
Drives team on proactively identifying and anticipating new cross-solution opportunities for Consumption, Usage & Unified expansion (especially Enhanced Solutions) at scale based on business value to customer/partner and clear understanding of the Microsoft value proposition for supported platforms to empower cloud success, foster and strengthen security and resiliency, and drive AI innovation.. Executes change management to ensure team members develop thorough understanding of customer's business and operations to advise on potential solutions. Coaches team on strategically consulting with, actively listening to and respectfully challenging customers/partners, building trust to then advocate for alternative architectures/solutions/approaches that shape and/or enhance customer requirements. Leads team on identifying, anticipating, and evaluating industry trends (e.g., customer industry verticals, information technology [IT] industry), gathering customer/partner insights (e.g., feedback around technical preferences, environments, business needs, competitive landscape), and mapping both existing and novel architecture and digital transformation solutions to customer/partner business outcomes. Leads teams to capture opportunities in appropriate systems, processes, and tools, working collaboratively across the organization to execute on opportunities aligned with Microsoft's Customer Engagement Model.
Customer Centricity
Coaches team to focusing on customer/partner experience through efficient delivery and ensuring a seamless and connected customer experience. Drives team on realization of customer/partner conditions of success by leveraging an understanding customer goals. Leads team on anticipating, addressing, and leading customer confidence calls to resolve customer/partner dissatisfaction and unmet needs, and creating and executing strategies to improve customer experience, value realization, and acceleration of transformation.
Supports and coaches team on proactively identifying and/or translating customer/partner problems into industry solutions aligned with Microsoft product and platform strategy, creating or improving an existing business model, and explaining why and/or how they meet customer/partner outcomes and return on investment (ROI) goals (e.g., via proof of concept, minimally viable product [MVP], rapid prototype) relative to competitive offerings. Leads team on proactively helping the customer/partner accelerate their adoption and use of Microsoft product/platform strategy-aligned (cross-solution area) solutions. Coaches team on building relationships with, and providing direction to C-suite level technical decision makers (TDMs) up to the C-suite level, and building the bridge between TDMs and business decision makers (BDMs). Leads and elevates interactions needed with customers/partners' Chief Information Office (CIO), Chief Information Security Officer (CISO), and other C-level (CXO) roles to bridge understanding of security, compliance, operational and risk requirements across BDMs, TDMs, CIO and CISO teams, appropriately engaging additional subject matter experts when deeper expertise is required.
Leads team on proactively acting as the voice of the customer/partner, sharing ideas, feedback, insights, success stories, and strategic/technical input with Engineering teams, Product Offerings teams and internal communities, leveraging relevant insights from feedback tools and systems. Coaches team on identifying and aggregating patterns across customers/partners/territories/industries and leveraging them with relevant industry perspective to develop strategic and actionable insights. Guides team on presenting business cases to program managers to advocate for and influence product roadmaps, decision making, bug fix prioritization, and own and drive initiatives as appropriate.
Partner Specialization
Actively orchestrates initiatives. Leverages an expansive knowledge of the partner portfolio in their respective solution areas to select and champion the most qualified partners to fulfill complex customer project or needs. Collaborates with Global Partner Solutions (GPS) business stakeholders and Partner Technology Specialist (PTS) leadership in order to lead and oversee the execute initiatives to translate opportunities into action. Fosters technical and solutions coherence across Microsoft partner and sales ecosystems to ensure precise alignment for a specific customer opportunity.
Concentrates on the identification and prioritization of opportunities that resonate with the organization's strategic revenue goals. Directs and elevates the support provided by partner and sales teams to deliver decisive, influential guidance for specific opportunities through proof of concept and technical pre-sales support.
People Management
Managers deliver success through empowerment and accountability by modeling, coaching, and caring. Model: Live our culture. Embody our values. Practice our leadership principles. Coach: Define team objectives and outcomes. Enable success across boundaries. Help the team adapt and learn. Care: Attract and retain great people. Know each individual’s capabilities and aspirations. Invest in the growth of others.
Technical Leadership
Leads team on proactively identifying and anticipating gaps through delivery, communicating those gaps to relevant team members and internal/external stakeholders, and connecting gaps and patterns across business and technology areas that drive changes and improvements to products, IP (both existing and new), technologies, and/or processes/practices that enable solutions to scale across customers. Coaches team on developing and contributing to modifications of Microsoft's structured frameworks and methodologies. Leads team on providing thought leadership and innovation to customers/partners and internal communities at the worldwide level.
Coaches and enables team on leading, mentoring, and maybe assembling virtual teams (v-teams) around technologies and customer/partner challenges, sharing ideas, insights, and strategic, technical input with technical teams, internal communities across the field, and the larger v-team across Microsoft using knowledge of Microsoft architectures and their context in the competitive landscape. Coaches team on demonstrating deep industry knowledge and driving recognition for Microsoft solutions by leading presentations and engagements with external and internal audiences (e.g., Tech Connect, Build, Ignite) Leads team on contributing to and collaborating on intellectual property (IP) and proactively identifying patterns where no IP exists (e.g., CoPilot and AI scenarios) to help build scalable and repeatable models. Participates in and leads external technical and non-technical community events (e.g., conferences, seminars, technical meetups, webcasts, blogs, hackathons) that elevate the Microsoft brand, sharing learnings across internal teams and leading their team to do the same.
Supports and guides team on leveraging market insights and demand signals to assist leadership in identifying relevant areas in which to drive up-skilling and/or accreditations based on demand. Drives team on role modeling technical readiness, both depth-aligned to solution area priorities and breadth-aligned to Customer Success Unit (CSU)/corporate initiatives (e.g., security, resilience, AI), and influencing team to drive their own technical readiness. Coaches team on acting as a mentor, leading readiness and upskilling activities in the team/organization by educating colleagues on technical and non-technical concepts and sharing proven practices. Ensures team members dedicate time to continuous upskilling efforts, monitoring their efforts and providing guidance where appropriate.
Qualifications
Required Qualifications:
Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, Liberal Arts, or related field AND 8+ years experience in cloud/infrastructure technologies, information technology (IT) consulting/support, systems administration, network operations, software development/support, technology solutions, practice development, architecture, and/or consulting OR equivalent experience.
3+ years people management experience, including managing consultant practice managers, technical sales managers, and/or technical architect managers.
Other Requirements:
- Microsoft is unable to sponsor a work visa for this role due to the nature of the role's job duties.
Preferred qualifications:
Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, Liberal Arts, or related field AND 12+ years experience in cloud/infrastructure technologies, information technology (IT) consulting/support, systems administration, network operations, software development/support, technology solutions, practice development, architecture, and/or consulting OR Master's Degree in Computer Science, Information Technology, Engineering, Business, Liberal Arts, or related field AND 8+ years experience in cloud/infrastructure technologies, technology solutions, practice development, architecture, and/or consulting OR equivalent experience.
6+ years experience working in a customer-facing role (e.g., internal and/or external).
6+ years experience leading technical projects, teams, or functions.
Technical Certification in Cloud (e.g., Azure, Amazon Web Services, Google, security certifications).
5+ years people management experience, including managing consultant practice managers, technical sales managers, and/or technical architect managers.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
#MCAPSA
Cloud Solution Architecture M5 - 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: $170K across 217 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 37,339 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($201K) sits 31% above the category median. Disclosed range: $130K to $272K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Microsoft AI Hiring
Microsoft has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $272K - $272K.
Location Context
AI roles in New York pay a median of $204,100 across 1,633 tracked positions. That's 7% above 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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