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About This Role
Overview
Leads and develops a high\-performing team of AI Business Solutions Specialists responsible for helping customers transform how people work, how business processes operate, and how organizations create value through Microsoft AI, Copilot, agents, automation, business applications, low\-code platforms, and cloud technologies. Builds a culture of accountability, customer obsession, innovation, continuous learning, and execution excellence while driving business growth across both productivity\-led and business\-process\-led transformation opportunities. Champions a consultative, outcome\-focused sales culture that moves customer conversations beyond technology adoption to organizational transformation and measurable business impact. Serves as a senior business leader who shapes go\-to\-market strategy, influences organizational priorities, and drives execution across Microsoft sales, customer success, partner, engineering, and delivery organizations. Partners with senior leaders to identify growth opportunities, remove barriers, accelerate customer outcomes, and strengthen Microsoft’s leadership in AI\-powered business transformation. Leads teams in helping customers reimagine employee productivity, decision\-making, customer engagement, business operations, and end\-to\-end business processes through Copilot, agents, AI\-powered applications, automation, analytics, and emerging AI technologies. Coaches specialists to lead executive conversations focused on business outcomes, transformation roadmaps, change management, value realization, and long\-term strategic impact. Champions operational excellence through disciplined business management, forecasting, pipeline rigor, consumption growth, and data\-driven decision making. Uses market intelligence, customer insights, competitive signals, and AI\-powered analytics to guide investments, identify emerging opportunities, and improve business performance across the territory. Acts as a leader beyond the immediate team by driving organizational change, scaling best practices, influencing strategic initiatives, and building future leadership capabilities. Serves as an executive sponsor for priority customers and strategic opportunities while helping shape Microsoft’s broader AI business transformation strategy. In addition, this role is responsible for attracting, developing, and retaining exceptional talent; building future leaders; fostering an inclusive, high\-performing culture; managing employee performance; and ensuring the organization is positioned to achieve both near\-term business objectives and long\-term growth.
Responsibilities Customer Engagement
- Lead and mentor a team of specialists in developing trusted advisor relationships with senior business and technology decision makers across strategic customers.
- Coach specialists to lead discovery\-first engagements that uncover business priorities, quantify desired outcomes, align stakeholders, and create urgency for change.
- Help customers develop enterprise AI strategies that connect employee productivity, business process transformation, data, security, and organizational change into a unified business vision.
- Ensure teams translate customer priorities into differentiated transformation strategies, compelling business cases, working prototypes, measurable value realization plans, and scalable adoption programs.
- Coach specialists to engage C\-suite executives, business decision makers, line\-of\-business leaders, operations leaders, HR, finance, sales, customer service, and IT stakeholders.
- Serve as an executive sponsor for priority customers and strategic opportunities, helping remove barriers, align stakeholders, and accelerate business decisions.
- Drive conversations focused on workforce transformation, business process modernization, AI adoption, change management, customer outcomes, and long\-term business value.
- Ensure customer engagements demonstrate Microsoft’s commitment to security, compliance, privacy, and responsible AI throughout the customer journey.
- Position services, support, adoption, and change management as critical components of customer success and value realization.
People Leadership \& Culture
- Build and sustain a high\-performance sales culture grounded in accountability, customer obsession, collaboration, innovation, inclusion, and continuous growth.
- Model Microsoft’s culture, values, and leadership principles through everyday leadership, customer engagement, coaching, and decision making.
- Establish clear team objectives, performance expectations, and measurable outcomes aligned to business priorities.
- Coach specialists to become strategic, consultative sellers capable of leading executive conversations, navigating complex stakeholder environments, and driving transformational outcomes.
- Build a culture where specialists understand and articulate Microsoft’s end\-to\-end AI transformation story rather than individual products or solution categories.
- Develop talent through coaching, mentorship, succession planning, and individualized growth plans to create future leaders.
- Invest in business, industry, technical, AI, and competitive fluency across the team to strengthen customer credibility and consultative selling capabilities.
- Create an inclusive environment where employees receive direct feedback, understand expectations, and are empowered to achieve their potential.
- Lead the team through market, technology, organizational, and customer\-driven change with clarity and confidence.
- Recognize and scale behaviors that contribute to sustainable customer success and business performance.
- Manage performance with fairness, urgency, accountability, and a commitment to employee development.
Sales \& Pipeline Management
- Own the team’s business performance and establish an operating rhythm that connects strategy, execution, pipeline health, forecast accuracy, consumption growth, and customer outcomes.
- Lead rigorous pipeline and business reviews with specialists and senior stakeholders to improve opportunity quality, sales execution, forecasting, and accountability.
- Establish clear standards for opportunity qualification, progression, close planning, consumption milestones, and customer success handoffs.
- Use AI\-powered analytics, usage data, propensity insights, renewal signals, and customer health indicators to prioritize investments and guide execution.
- Ensure sales and consumption pipeline data is accurate, complete, and aligned with customer commitments and business outcomes.
- Drive early orchestration with Account Teams, Solution Engineering, Customer Success, Services, Global Partner Solutions, and partners to reduce execution risk and accelerate outcomes.
- Lead teams in identifying and addressing deal risk, adoption challenges, consumption gaps, and competitive threats.
- Develop scalable operating practices and interventions that improve sales performance across the broader organization.
- Balance near\-term revenue and consumption goals with long\-term pipeline health and customer value realization.
Sales Strategy \& Business Growth
- Lead comprehensive territory planning and business analysis to identify whitespace, market shifts, competitive threats, emerging customer priorities, and new growth opportunities.
- Develop and execute strategies that protect and expand existing business, accelerate customer transformation, and create new opportunities for long\-term growth.
- Guide teams in positioning Microsoft as the strategic AI platform connecting employee experiences, business applications, business processes, enterprise data, and security.
- Lead teams in identifying opportunities that span Microsoft 365 Copilot, Dynamics 365, Power Platform, Copilot Studio, Agents, Work IQ, and future AI\-driven solutions.
- Coach specialists to lead value\-based customer conversations that focus on business outcomes, productivity gains, operational efficiency, customer experience, and growth.
- Develop strategies that expand AI adoption from individual productivity improvements to organization\-wide transformation initiatives.
- Drive customer conversations around agentic workflows, AI\-enabled operating models, business process modernization, and next\-generation business applications.
- Shape and sponsor new sales motions, customer engagement models, and solution strategies that respond to evolving customer and market demands.
- Influence senior stakeholders on resource allocation, investment priorities, strategic initiatives, and go\-to\-market execution.
- Continuously evaluate performance, customer feedback, and market intelligence to refine sales strategies and improve business results.
Partner \& Cross\-Organizational Leadership
- Build strong relationships across Account Teams, Customer Success, Solution Engineering, Industry Solutions Delivery, Global Partner Solutions, Services, Marketing, and Product organizations.
- Create strong collaboration across AI Workforce, AI Business Process, Data \& AI, Security, Infrastructure, and Customer Success teams to deliver integrated customer outcomes.
- Align stakeholders around shared customer goals, execution plans, ownership models, and success metrics.
- Lead orchestration of complex customer pursuits across multiple organizations and solution areas.
- Establish effective handoffs that preserve business context, customer outcomes, success criteria, and accountability throughout the customer lifecycle.
- Develop strategic partner relationships that increase solution capacity, improve time\-to\-value, and strengthen Microsoft’s market position.
- Drive partner attachment and co\-sell strategies that accelerate adoption, deployment, value realization, and customer success.
- Resolve organizational barriers and competing priorities through influence, credibility, and strong leadership.
- Create feedback loops between the field and engineering, product, marketing, and enablement teams to improve field readiness and customer impact.
Innovation \& Organizational Impact
- Act as a leader beyond the immediate team by shaping broader business priorities, sharing strategic insights, and contributing to cross\-organizational initiatives.
- Scale successful transformation patterns, customer success stories, best practices, and sales motions across the broader organization.
- Encourage thoughtful experimentation with AI, agents, automation, copilots, and emerging technologies to improve seller effectiveness and customer outcomes.
- Establish measurable success criteria for new motions and use data\-driven insights to determine when to scale or adjust approaches.
- Anticipate changes in customer demand, competitive dynamics, technology trends, and AI adoption patterns, translating those changes into action plans for the team.
- Represent customer and field perspectives in leadership discussions that influence product strategy, investments, programs, and go\-to\-market priorities.
- Maintain disciplined execution of the current business while building the capabilities, skills, partnerships, and opportunities required for future growth.
- Create durable organizational impact through talent development, strategic influence, operational excellence, and measurable customer outcomes.
Role Success Profile
The successful leader in this role is equally comfortable discussing employee productivity transformation, AI\-powered business processes, Copilot adoption, agentic automation, Dynamics 365, Power Platform, organizational change, and business strategy. They build exceptional teams, drive operational rigor, influence beyond organizational boundaries, and help customers transform how work gets done through AI.
They do not simply lead a sales team. They build a business, develop talent, shape strategy, and help customers become AI\-powered organizations.
Qualifications Required/minimum qualifications
Bachelor's Degree in Computer Science, Information Technology, Business Administration, Information Security, or related field AND 6\+ years experience in technology\-related sales or account management 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.
Additional or preferred qualifications
Master's Degree in Business Administration (i.e., MBA), Information Technology, Information Security, or related field AND 8\+ years experience in technology\-related sales or account management OR Bachelor's Degree in Computer Science, Information Technology, Business Administration, Information Security, or related field AND 12\+ years experience in technology\-related sales or account management OR equivalent experience.
6\+ years solution or services sales experience.
3\+ years people management experience.
Solution Area Specialists M5 \- The typical base pay range for this role across the U.S. is USD $133,000 \- $222,700 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 $170,300 \- $239,800 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 $133K-$239K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($186K) sits 13% below the category median. Disclosed range: $133K to $239K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Microsoft AI Hiring
Microsoft has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span US, CA, US, Redmond, WA, US. Compensation range: $147K - $331K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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