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About This Role
About Vantage Data Centers
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Vantage Data Centers powers, cools, protects and connects the technology of the world’s well\-known hyperscalers, cloud providers and large enterprises. Developing and operating across North America, EMEA and Asia Pacific, Vantage has evolved data center design in innovative ways to deliver dramatic gains in reliability, efficiency and sustainability in flexible environments that can scale as quickly as the market demands.
Information Technology Department
The Information Technology Department architects and operates the digital foundation powering Vantage Data Centers' global operations while pioneering the intelligent transformation of how we work. Our integrated IT/AI organization delivers enterprise\-grade infrastructure, networking, cybersecurity, and end\-user services with operational excellence, while simultaneously developing artificial intelligence capabilities that drive measurable business value. We are transforming our service delivery model around ITIL best practices, comprehensive automation frameworks, and governance structures that enable both stability and innovation. Our teams collaborate across Infrastructure, Network, Service Desk, IT Engineering, and Artificial Intelligence functions, emphasizing service over technology and recognizing that success is measured by business outcomes rather than technical implementations. This is an exceptional time to join as we fundamentally reshape how technology serves the organization, creating an environment where operational discipline and innovative experimentation work as complementary forces.
Artificial Intelligence Team
The Artificial Intelligence Team exists to amplify human potential through intelligent technology. We're building an AI\-powered ecosystem that transforms how Vantage creates value, freeing our people from routine work so they can focus on innovation and strategic thinking that truly differentiates us in the market. Through responsible AI implementation across operations, engineering, and business functions, we're not just automating processes; we're reimagining what's possible when human creativity partners with artificial intelligence. Our approach centers on measurable business outcomes, ethical governance, and creating capabilities that make every team member more effective while positioning Vantage at the forefront of the intelligent infrastructure revolution.
Position Overview
This position is based remotely in the United States.
The AI Solutions Engineer, Global is a hands\-on technical builder within Vantage’s Accelerators team, responsible for transforming validated business requirements into working AI prototypes that demonstrate tangible value within aggressive 6\-week development cycles. This role combines deep expertise in AI/ML technologies, Microsoft 365 ecosystem mastery, and pragmatic software engineering discipline to deliver production\-quality code under rapid prototyping constraints. The Solutions Engineer operates at the technical frontline of innovation, making real\-time architectural decisions, writing code that balances speed with maintainability, and validating hypotheses through working demonstrations rather than theoretical designs. Success requires the rare combination of technical depth, velocity orientation, and business pragmatism, knowing when to leverage existing platforms versus custom development, when to optimize versus ship, and when technical debt is strategic versus dangerous. This is not research engineering focused on algorithmic innovation; this role delivers practical solutions using proven technologies in novel combinations that solve actual business problems for Vantage’s data center operations.
Essential Job Functions
- Design and deliver AI\-powered prototypes within 6‑week sprint cycles, producing production\-quality code that demonstrates business value and supports future enterprise productization.
- Build intelligent solutions leveraging the Microsoft 365 ecosystem, including Azure AI Services, Azure OpenAI, Copilot Studio, and Power Platform.
- Partner with Accelerators and Navigators to translate validated opportunities into working solutions, providing feedback on feasibility, performance, and implementation complexity.
- Apply SDLC best practices—version control, code reviews, automated testing, and CI/CD—within a rapid prototyping environment to balance quality and speed.
- Produce clear technical documentation (code, APIs, deployment, and architecture decisions) to enable seamless handoff to Development teams.
- Demonstrate prototypes to stakeholders, translating technical capabilities into business value and incorporating feedback into iterative improvements.
- Make pragmatic engineering decisions that balance speed, technical debt, and long\-term scalability.
- Develop solutions using Halo and Copilot platforms in alignment with enterprise architecture and platform standards.
Duties
- Develop AI solutions using Python, TypeScript/JavaScript, and modern frameworks, including ML models, agentic systems, NLP, and intelligent automation.
- Integrate Azure AI services such as Azure OpenAI, Azure Machine Learning, Cognitive Services, and Document Intelligence into enterprise\-ready solutions.
- Build custom Copilots using Copilot Studio to support conversational AI, workflow automation, and decision support within Microsoft 365\.
- Design data pipelines and ETL processes to prepare enterprise data for AI use, ensuring data quality, privacy, and performance.
- Implement APIs, webhooks, and integrations connecting AI solutions with enterprise platforms (Microsoft 365, SharePoint, Power Platform, Salesforce, and operational systems).
- Rapidly evaluate and experiment with emerging AI technologies, focusing on solutions suitable for production environments.
- Participate in code reviews, pair programming, and knowledge sharing to ensure prototype quality and team capability growth.
- Embed security, privacy, and governance controls from the outset, meeting enterprise compliance standards.
- Create automated tests and validation checks to verify solution accuracy, reliability, and readiness for demonstrations.
- Troubleshoot and resolve complex technical and integration issues while documenting learnings.
- Package prototypes with deployment scripts, configuration documentation, and user guides for stakeholder demonstrations.
- Support handoff to Development teams through architecture walkthroughs, documentation, and knowledge transfer.
- Stay current on Microsoft AI platform updates and industry best practices, applying relevant innovations to prototyping efforts.
- Handle additional duties as assigned by Management.
Job Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or related field; advanced degree or relevant AI/ML certifications preferred.
- Strong hands\-on experience building AI/ML solutions using modern frameworks (e.g., LangChain, Semantic Kernel, or similar agentic platforms).
- Proficiency in Python and TypeScript/JavaScript, with a track record of writing clean, maintainable, well\-documented code.
- Deep experience with Microsoft Azure, including Azure OpenAI, Azure AI Services, Azure Functions, Logic Apps, and Power Platform.
- Experience developing within the Microsoft Copilot ecosystem (Copilot Studio, Microsoft Graph, Teams, SharePoint).
- Solid understanding of machine learning, NLP, retrieval\-augmented generation, prompt engineering, and agentic system design.
- Experience with modern development practices including Git, CI/CD pipelines, containerization, infrastructure as code, and cloud\-native architectures.
- Strong API and integration experience, including REST services, OAuth/authentication, and enterprise system connectivity.
- Proven ability to deliver high\-quality software under tight timelines while balancing speed and technical rigor.
- Familiarity with data center operations, construction workflows, or infrastructure delivery is a plus.
- Knowledge of security, data privacy, and enterprise compliance standards.
- Strong problem\-solving and debugging skills, particularly in complex, integrated environments.
- Excellent communication skills, with the ability to explain technical concepts to non\-technical stakeholders.
- Self\-directed, adaptable, and comfortable working in fast\-paced, minimally supervised environments.
- Demonstrated growth mindset with enthusiasm for learning, experimentation, and knowledge sharing.
- Travel required is expected to be up to 5% but may increase over time as the business evolves.
Physical Demands and Special Requirements
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is occasionally required to stand; walk; sit; use hands to handle, or feel objects; reach with hands and arms; climb stairs; balance; stoop or kneel; talk and hear. The employee must occasionally lift and/or move up to 25 pounds.
Additional Details
- Salary Range: $130,000\-135,000 Base \+ Bonus (this range is based on Colorado market data and may vary in other locations)
- This position is eligible for company benefits including but not limited to medical, dental, and vision coverage, life and AD\&D, short and long\-term disability coverage, paid time off, employee assistance, participation in a 401k program that includes company match, and many other additional voluntary benefits.
- Compensation for the role will depend on a number of factors, including your qualifications, skills, competencies, and experience and may fall outside of the range shown.
\#LI\-Remote \#LI\-CD1
We operate with No Ego and No Arrogance. We work to build each other up and support one another, appreciating each other’s strengths and respecting each other’s weaknesses. We find joy in our work and each other, actively seeking opportunities to inject fun into what we do. Our hard and efficient work is rewarded with an above market total compensation package. We offer a comprehensive suite of health and welfare, retirement, and paid leave benefits exceeding local expectations.
Throughout the year, the advantage of being part of the Vantage team is evident with an array of benefits, recognition, training and development, and the knowledge that your contribution adds value to the company and our community.
Don't meet all the requirements? Please still apply if you think you are the right person for the position. We are always keen to speak to people who connect with our mission and values.
Vantage Data Centers is an Equal Opportunity Employer
Vantage Data Centers does not accept unsolicited resumes from search firm agencies. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of Vantage Data Centers.
We’ll be accepting applications for at least one week from the date this role is posted. If you're interested, we encourage you to apply soon—we’re excited to find the right person and will keep the role open until we do!
Salary Context
This $130K-$135K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Vantage Data Centers, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($132K) sits 21% below the category median. Disclosed range: $130K to $135K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Vantage Data Centers AI Hiring
Vantage Data Centers has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, CO, US, CA, US. Compensation range: $135K - $210K.
Location Context
AI roles in Denver pay a median of $198,000 across 169 tracked positions. That's 8% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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