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About This Role
Overview
Technical Consultant, Cloud \& AI \- CTJ \- Poly
Participates in the pre\-sale process as needed. Helps scope the project by sharing customer or technical knowledge in their specialty domain by leveraging their expertise as a subject matter expert. Maintains tools with up\-to\-date skills and availability. Participates in and/or may lead meetings with customers/partners to understand their business needs and/or scenario they are trying to solve. Engages others appropriately to understand and define customer requirements. Communicates the business value of planned solutions to customers/ partners with direction/guidance. Implements mitigations on technical and business risks. Manages their schedule and communicates to project leads. Delivers against Work Breakdown Structure (WBS). Oversees aspects of implementation. Acts as an ambassador in consumption of intellectual property (IP) and leverages IP in projects. Proactively assists in the creation of intellectual property content and seeks areas that are available for more IP. Communicates areas that need refreshing or gaps in intellectual property. Provides feedback for continuous improvement. Identifies opportunities to expand or accelerate the adoption and consumption of the cloud and Microsoft technologies. Obtains relevant accreditations and certification(s) as advised by the domain leadership team.
Responsibilities
Technical Delivery
Implements technical solutions by completing assigned project tasks with defined quality standards and following Industry Solutions processes and security initiatives. Oversees aspects of implementation.
Follows the capacity process outlined by Resource, Insights, Capacity, and Capability (RICC) team. Maintains tools with up\-to\-date skills and availability.
Proactively manages relationships with customers/partners/stakeholders to identify and contribute to the drivers of satisfaction and dissatisfaction, determine the root cause, and establish recovery actions to improve the experience.
Supports project planning and the development of project documents by defining the risks and dependencies. Communicates the business value of planned solutions to customers/ partners with direction/guidance. Implements mitigations on technical, business, and security risks. Manages their schedule and communicates with project leads. Delivers against Work Breakdown Structure (WBS).
Participates in and/or may lead meetings with customers/partners to understand their business needs and/or scenario they are trying to solve. Uses business, technology, security, and industry strategies to define customer/partner requirements and constraints. Engages others (e.g., EAG Security stakeholders) appropriately to understand and define customer requirements, ensuring security is prioritized.
Proactively identifies issues and risks and engages with customers/partners or internal stakeholders (e.g., Project Managers) as appropriate to address and resolve issues while maintaining a security\-first mindset.
Business Development
Identifies opportunities to expand or accelerate the adoption and consumption of cloud and Microsoft technologies.
Intellectual Property Management
Acts as an ambassador in consumption of intellectual property (IP) and leverages IP in projects. Proactively assists in the creation of intellectual property content and seeks areas that are available for more IP. Communicates areas that need refreshing or gaps in intellectual property. Provides feedback for continuous improvement.
Operational Excellence
Completes operational tasks and readiness and ensures timeliness and accuracy while maintaining adherence to security standards through Delivery Compliance, Privacy, and Security (DCPS). Follows Microsoft policies, compliance, and procedures (e.g., Enterprise Services Authorization Policy, Standards of Business Conduct, labor logging, expenses, travel guidelines).
Pre\-Sales Support
Participates in the pre\-sale process as needed. Helps scope the project by sharing customer or technical knowledge in their specialty domain by leveraging their expertise as a subject matter expert.
Readiness
Learns new technologies or services based on business demands and industry trends and evaluates their impact on security posture, revisiting the Industry Solutions Delivery (ISD) Learning Portfolio for Security to deepen understanding of security considerations. Obtains relevant accreditations and certification(s) as advised by the domain leadership team. Shares experiences, best practices, and product news within the team. Participates in relevant technical communities at Microsoft.
Qualifications
Required/minimum qualifications
- Bachelor's Degree in Computer Science, Engineering, Finance, Business, or related field AND 1\+ year(s) work experience in relevant area of business OR equivalent experience.
Other RequirementsSecurity Clearance Requirements: Candidates must be able 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:
- The successful candidate must have an active U.S. Government Top Secret Clearance with access
to Sensitive Compartmented Information (SCI) based on a Single Scope Background Investigation
(SSBI) with Polygraph. Ability to meet Microsoft, customer and/or government security screening
requirements are required pre\-offer and post\-hire for this role. Failure to maintain or obtain the
appropriate U.S. Government clearance and/or customer screening requirements may result in
employment action up to and including termination.
- Clearance Verification: This position requires successful verification of the stated security
clearance to meet federal government customer requirements. You will be asked to provide
clearance verification information prior to an offer of employment.
- 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* 3\+ years work experience in relevant area of business.
- Technical certifications based on domain/service line (e.g., Azure, Security, Dynamics).
- Delivery Management certification (e.g., Scrum, Agile, Change Management, Project Management).
- Must have active Full Scope Poly
Technology Consulting IC3 \- The typical base pay range for this role across the U.S. is USD $77,900 \- $156,200 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 $100,800 \- $169,200 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 $77K-$169K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($123K) sits 44% below the category median. Disclosed range: $77K to $169K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Microsoft AI Hiring
Microsoft has 29 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span US, Redmond, WA, US, Dallas, TX, US. Compensation range: $143K - $304K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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