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About This Role
Overview
Our purpose is to empower organizations to transform the way they work by harnessing the full potential of artificial intelligence. We guide customers through the evolving digital landscape, enabling them to unlock new opportunities, enhance productivity, and deliver exceptional employee and customer experiences. By integrating advanced AI capabilities across devices, cloud platforms, and everyday business applications, we help organizations realize seamless, innovative, and secure solutions that drive sustained growth and success in the AI era.
In this role you will be the AI Business Process Finance \& Supply Chain Solution Engineer (SE) for the enterprise segment for your assigned workload and a member of the sales team that consists of AI Business Process Specialist (SSP), Customer Success Unit (CSU), partners and engineering.
As an AI Business Process Finance \& Supply Chain Solution Engineer (SE), you will lead AI transformation engagements with ERP domain expertise. Your role involves owning the technical win strategy and supporting AI Business Process Sales Specialists (SSP) by forming strong relationships with C\-Suite executives, Business Decision Makers (BDMs), and Technical Decision Makers (TDMs) like CIO, CTO, and IT Leaders. You will help them achieve their goals for product proficiency, roadmap, and compete discussions to secure technical decisions.
Your responsibilities include orchestrating and executing POCs, envision workshops, whiteboarding sessions, and conducting technical demonstrations to showcase the business value of D365 F\&SC, Copilot Studio, and LOB AI Agents offerings. You will proactively collaborate with partners early in the sales cycle.
You will be part of a dedicated sales community supported by Account teams, Marketing, Engineering, and Customer Success teams that enable you to drive enterprise\-wide adoption of D365 solutions. You will secure competitive wins against key competitors by showcasing Microsoft’s unique differentiation, One Microsoft narrative, supporting migration and replacement technical plans, and addressing technical blockers related to Copilot and AI Agents compliance, privacy, and security concerns.
As a Solution Engineer, you will collaborate with SI partners early in the sales cycle to scale technical engagements—such as demos, whiteboarding sessions, and POCs—supporting customers effectively. You will be responsible for increasing D365 F\&SC revenue by generating new pipeline creation, increased deal velocity and competitive share capture by supporting SSP in addressing technical proof needs.
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 Build Strategy
- Acts as the voice of the customer (VOC) by driving new feedback, blockers, insights, and resources (e.g., unified action tracker \[UAT], technical feedback platform) across territories so they can be added and prioritized. Represents the customer to product teams (e.g., Engineering) to shape products and services by providing insights across the territory.
- Captures core customer compete knowledge across solution areas and delivers back to product and engineering teams to enhance team capabilities and develop compete strategies.
- Provides strategic, technical, and partner input based on Microsoft capability to contribute to strategy development, leveraging partner and competitor knowledge.
- Works with account teams to shape strategic customer win strategy and tailor unique Microsoft value messaging to audience.
- Monitors and analyzes action plans to promote customer usage of key/prioritized Microsoft solutions/products that support customer’s business outcomes. Identifies opportunities to promote usage. responsibility description
Scale Customer Engagements
- Uses knowledge of customer context, cross\-solution, or portfolio expertise, and technical and market/industry knowledge to build credibility with customers individually or at scale.
- Leverages knowledge of programs and investments (e.g., Microsoft Technology Center \[MTC], exec briefing center, partner workshops) and proactively engages with engineering, product, and support teams to remediate blockers.
- Helps ensure consistency and quality of engagements through adherence of standards and best practices (e.g., managed service provider \[MSP], managed certified professional \[MCP]). Utilizes common sales and delivery methodology for the Microsoft sales organization to identify and engage customers in new opportunities. Actively seeks and incorporates feedback on customer impact to increase capabilities.
- Proactively helps to maximize impact of customer engagements by executing on solutions to blockers.
- Engages with customer technical and business decision makers, and identifying customer needs and issues proactively through technical discovery.
- Uses knowledge of customer context, cross\-solution or portfolio expertise, and deep technical and market/industry knowledge to build credibility with customers individually or at scale.
- Leads and ensures customer needs are met by showcasing suitable Microsoft solutions anchored in insights and business value, and influences customer technical buy\-in. Utilizes rules of engagement (e.g., role boundaries, handoff strategies), and leverages knowledge of processes (e.g., Managed Service Provider \[MSP], co\-sell partners), tools, and programs (e.g., FastTrack). Searches for customer references to use in engagements.
Scale Through Partners
- Engages in partner sell\-with scenarios by acting as liaison between the partner and team and shares visibility on partner resources and processes. Supports the development and maintenance of relationships with pre\-sale team and partners. Self\-identifies and selects top 5 to 10 go\-to partners on proactive and early engagement in opportunities.
Solution Design and Proof
- Presents, applies, and customizes architecture patterns to partners or customers or businesses and drives cross\-workload support for Microsoft Solutions.
- Applies and customizes existing demonstration assets. Demonstrates and oversees demonstrations (e.g., pre\-built demos, architectural design sessions \[ADS], proof of concept \[POC] sessions with partners) of solutions based on multiple Microsoft products through early stage engagements. Leverages partner/customer teams as needed to prove capabilities and integration into customer environment. Reviews partner demonstrations and provides feedback to ensure alignment with Microsoft standards.. responsibility description
Technical Leadership
- Monitors and responds to internal and external tech community posts, attends community calls, sessions, hackathon, etc., and acts as a mentor for their technology area. Contributes and participates in Customer Executive Briefing Center sessions. Shares best practices internally on community calls and drives recognition of Microsoft cloud solutions through presentations and engagements with external audiences.
- Conducts group training or one\-to\-many events (e.g., workshops, Webinars) or leverages existing scale enablement programs to present and educate customers and colleagues on the capabilities and benefits across Microsoft solutions/products.
- Grows domain knowledge and practices expertise by communicating with customers, partners, and senior colleagues to expand knowledge of architecture. Builds their own readiness plan and proactively identifies learning gaps.
Qualifications Required/minimum qualifications
Bachelor's Degree in Computer Science, Information Technology, Engineering or related field AND 4\+ years technical pre\-sales or technical consulting experience OR equivalent experience.
Additional or preferred qualifications
Bachelor's Degree in Computer Science, Information Technology, Engineering or related field AND 8\+ years technical pre\-sales or technical consulting experience OR equivalent experience.
4\+ years experience with cloud and hybrid, or on premises infrastructures, architecture designs, migrations, industry standards, and/or technology management.
Certification in relevant (Microsoft or industry) technologies or disciplines (e.g., Microsoft Office 365; Power BI; Azure Administrator, Architecture, and Development exams; Cloud Platform Technologies; Information Security; Architecture).
Digital Solution Engineering IC4 \- The typical base pay range for this role across the U.S. is USD $106,400 \- $203,600 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 $137,600 \- $222,600 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 $106K-$222K range is below 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($164K) sits 23% below the category median. Disclosed range: $106K to $222K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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