Interested in this AI/ML Engineer role at Rackspace Technology?
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Job Profile Summary
The Technical Evangelist is responsible for building technical awareness, knowledge, and capability across internal teams, partners, and customers through enablement, education, and thought leadership. The role develops and delivers technical training and certification programs, enables partner organizations, and creates content that communicates product capabilities, use cases, and competitive positioning. It serves as a technical spokesperson at industry events and customer-facing forums, translating complex technologies into clear and compelling narratives for technical and business audiences. The role collaborates across Sales, Product, Marketing, and partner organizations to strengthen market awareness, partner effectiveness, and customer understanding.
Career Level Summary
- Recognized as an external thought leader within a strategic organization function or job discipline and requires broad and comprehensive expertise in leading-edge theories, techniques and/or technologies within own field.
- Proactively identifies and solves problems that impact the management and direction of the business.
- Contributes to the development of the organizational function strategy or product or business strategy.
Progression to this level is typically restricted on the basis of individual capabilities and business requirements. *
Critical Competencies
- Excellence: Exceeds expectations by consistently demonstrating accountability, discipline, high performance, and a proven track record of exceptional results.
- Customer-driven: Prioritizes customer needs and satisfaction through collaborative and proactive problem-solving, and an unwavering commitment to customer success.
- Expertise: Possesses deep understanding of customer needs and continually grows and enhances skills to provide customer-focused solutions.
- Agility: Quickly adapts and responds to dynamic customer needs and expectations through innovative solutions.
Compassion: Cultivates a positive and supportive environment to effectively work together towards a common goal, fostering trust within Rackspace and with external stakeholders. *
Key Responsibilities
- Builds and delivers training/certification programs for internal Sales, SDR, and channel partners on GPUaaS capabilities, use cases, and competitive positioning.
- Runs partner enablement — onboarding new ISV/channel partners, keeping their teams current on product and pricing.
- Produces thought-leadership content (blog posts, LinkedIn posts, whitepapers, webinars) that builds market awareness and credibility for the GPUaaS offering.
- Represents GPUaaS at industry events, conferences, and customer-facing forums as a technical spokesperson.
- Supports customer-facing technical narrative in top-of-funnel and mid-funnel moments (e.g., webinars, technical AMAs) — distinct from Sales' deal-specific technical validation.
- Measured on number of reps/partners trained and certified, content published and its reach/engagement, and event/program participation.
In GPUaaS: Owns the enablement and awareness layer that makes Sales, SDR, and channel partners effective sellers of GPUaaS — training internal teams and partner organizations, and publishing content that builds external awareness and thought leadership for the business. *
Knowledge
- Expert-level knowledge of AI infrastructure, GPU technologies, GPUaaS solutions, cloud computing, and related use cases and trends.
- Expert-level knowledge of translating complex technologies into compelling narratives and authoritative technical content.
- Expert-level knowledge of developing scalable technical training, enablement, and certification programs for internal and external audiences.
Expert-level knowledge of the AI/cloud landscape, competitive environment, customer needs, and partner ecosystems. *
Skills
- Business/Technical Consulting
- Cloud Computing
- Development/Training
- Digital Media Savvy
- Networking/Relationship Management
- Presentation Building
- Public Speaking
- Stakeholder Management (external/internal)
- Technical Acumen
Technical Writing Skills *
Education
Bachelor's Degree required, preferably in field related to role. At the manager’s discretion, additional relevant experience may substitute degree requirement.
Experience
15+ years of experience in the field of role required
#LI-Remote #LI-DB1
Our compensation reflects the cost of labor across several geographic markets. The compensation range for this position ranges from $185,709.00/year in our lowest geographic market up to 272,373.20 USD/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. The compensation package may also include incentive compensation opportunities in the form of annual bonus or incentives, equity awards and an Employee Stock Purchase Plan (ESPP). Learn more about benefits at Rackspace. About Rackspace Technology
We are the multicloud solutions experts. We combine our expertise with the world’s leading technologies — across applications, data and security — to deliver end-to-end solutions. We have a proven record of advising customers based on their business challenges, designing solutions that scale, building and managing those solutions, and optimizing returns into the future. Named a best place to work, year after year according to Fortune, Forbes and Glassdoor, we attract and develop world-class talent. Join us on our mission to embrace technology, empower customers and deliver the future.
More on Rackspace Technology
Though we’re all different, Rackers thrive through our connection to a central goal: to be a valued member of a winning team on an inspiring mission. We bring our whole selves to work every day. And we embrace the notion that unique perspectives fuel innovation and enable us to best serve our customers and communities around the globe. We welcome you to apply today and want you to know that we are committed to offering equal employment opportunity without regard to age, color, disability, gender reassignment or identity or expression, genetic information, marital or civil partner status, pregnancy or maternity status, military or veteran status, nationality, ethnic or national origin, race, religion or belief, sexual orientation, or any legally protected characteristic. If you have a disability or special need that requires accommodation, please let us know.
Salary Context
This $185K-$272K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Rackspace Technology, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($229K) sits 8% above the category median. Disclosed range: $185K to $272K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Rackspace Technology AI Hiring
Rackspace Technology has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Riyadh, Saudi Arabia. Compensation range: $272K - $272K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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