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Overview
We are looking for a Director, AI Business Process Leader - Microsoft US Federal to join our team.
In this leadership position, you will guide and develop a team responsible for advancing customer success through Microsoft’s AI and cloud solutions. You will align the team with the organization’s vision, sales frameworks, and business priorities while helping them identify market opportunities, address challenges, and lead end-to-end customer transformation initiatives. The role partners closely with area and organizational leaders to manage pipelines, maintain accurate forecasts, and execute business strategy across core solution areas. You will also strengthen collaboration across teams, connect Microsoft solutions with customer and partner needs, and share market feedback that helps refine strategy and build trust. As a people manager, you will support employee growth and development, oversee project execution, and manage team performance.
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
Customer Engagement and Partnership
- Serves as a customer and partner advocate within the subsidiary/area, offering feedback to solution area leaders to refine strategy and encourage continuous improvement to seize market opportunities and build trust. Advocates for a focus on customer success and supports the development of tailored strategies that meet their unique needs and goals.
- Encourages the early engagement of strategic partners in the sales process to enhance customer adoption and facilitate value realization.
- Participates in various rhythms of business (RoB) within the enterprise, taking into account both external (e.g., customer) and internal (e.g., sellers, internal teams) viewpoints.
- Delivers insights and feedback to internal teams to improve go-to-market (GTM) strategies and execution. Guides team in identifying and aligning sales plays with market and customer needs.
- Actively seeks to match solution scenarios with customer and partner organizations, focusing on how Microsoft's offerings can complement and add value to their business operations.
- Acts as a trusted advisor to customers and partners, utilizing insights to foster marketplace success.
- Engages in conversations with customers that highlight the strategic relationship between Microsoft, partners, and customers' business goals, thereby nurturing ongoing business partnerships.
- Advocates for customer support resources through Unified Support that aid in accelerating business value realization and transformation.
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 people. Know each individual’s capabilities and aspirations. Invest in the growth of others.
Sales Operations and Pipeline Management
- Facilitates cross-team collaboration to enhance operational excellence and support the execution of the business strategy. Works to simplify complexity within solution areas and contributes to the development of go-to-market (GTM) strategies for cross-solution plays that support business outcomes.
- Coordinates the integration of solutions across the full technology stack, engaging stakeholders to demonstrate the business value proposition. Works with team to identify gaps and facilitate the creation of development plans that build technical sales acumen and capabilities across the team.
- Guides team to utilize technology (e.g., automation, Power Platforms, Sales Agents) to foster business growth across solution areas and ensures the use of artificial intelligence (AI)-powered analytics and forecasting tools to enhance precision.
- Encourages team to identify new revenue streams across the subsidiary and supports sales execution efforts to capitalize on market-specific opportunities and align with AI-enhanced sales methodologies and practices.
- Implements aspects of the enterprise sales strategy, adapting the corporate framework into practical plans within the local market context to enhance revenue and business growth. Manages and directs organizational resources and helps build a sales pipeline to gain market share and compete in key growth areas.
- Helps identify opportunities for customers to use technology in ways that transform their business operations and accelerate growth.
- Collaborates with area or organization leader to ensure an accurate forecast through pipeline management and a deal-based forecast in each of the core solution areas.
- Monitors management of the sales pipeline and opportunities by reviewing overall pipeline progress to identify stalled deals and areas where guidance is needed.
- Supports the active management of opportunities, helps address potential roadblocks, and provides input to ensure sales targets and business objectives are met.
- Assists in the development and execution of sales strategy to optimize return on investments (ROI) and optimizes resources that drive impact across the solution area with operational vigor.
Sales Strategy and Execution
- Guides and supports team in anticipating, identifying, and addressing threats and gaps in the market across all solution areas.
- Leverages deep knowledge and interpretation of market trends and artificial intelligence (AI)-driven market intelligence tools to generate insights, influencing decisions that drive the organization forward.
- Develops tailored business solutions that respond to customer demands and market signals and validates that proposals meet customers’ strategic needs.
- Implements and oversees strategic sales plans for each solution area that respond to customer demands, industry needs, local regulations, and market trends to stimulate business growth and capture total addressable market (TAM) across the subsidiary.
- Develops a pipeline of diverse talent that responds to the needs of the market and enhances the organization's ability to realize the total addressable market (TAM).
- Guides team to lead end-to-end artificial intelligence (AI) and cloud-driven transformation initiatives for customers. Implements AI- and cloud-powered wins that both enhance customer business capabilities and demonstrate the transformative impact of cutting-edge AI technologies and cloud services Coaches team on how to differentiate Microsoft’s offerings from stakeholders by leveraging unique selling propositions to create advantage and capture market interest.
- Encourages teams to pursue innovation and excel in achieving market impact with innovative solutions. Promotes innovation and agility through AI and cloud adoption, aligning solutions to customer priorities and business outcomes.
- Manages and aligns team with the organization's vision and customer success objectives, ensuring adherence to sales frameworks and guidelines.
- Leads conversations around leveraging innovative solutions, ensuring that these innovations create a noticeable impact on the market.
- Promotes the integration of security principles in all customer engagements to uphold trust and compliance.
- Encourages innovation and problem-solving within team to stay ahead in the technical sales landscape.
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:* Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
- Citizenship & Citizenship Verification: This position requires verification of U.S. citizenship due to citizenship-based legal restrictions. Specifically, this position supports United States federal, state, and/or local United States government agency customer and is subject to certain citizenship-based restrictions where required or permitted by applicable law. To meet this legal requirement, citizenship will be verified via a valid passport, or other approved documents, or verified US government Clearance
Preferred Qualifications:* Preference for a current active clearance or the ability and willingness to obtain a clearance.
- 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: $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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($186K) sits 12% below the category median. Disclosed range: $133K to $239K.
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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