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About This Role
About Vantage
Vantage 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.
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
The Director, AI Platforms leads Vantage’s AI platform strategy and operations, owning the Halo platform roadmap and Vantage’s AI FinOps practice while managing go-to-market packaging, program governance, and cross-team coordination for the entire AI organization. The Director translates the AI strategy set by the Head of AI into the Halo roadmap and delivers it, making Halo, Vantage’s secure, governed AI platform, easy for employees and business builders to access and use, and giving Vantage clear visibility into what its AI consumption costs and where that spend goes. This role owns three interconnected mandates: ensuring platform reliability and continuous improvement, driving enterprise-wide adoption through compelling value narratives, and providing the operational backbone that enables the Navigators and Accelerators teams to execute effectively. The Director builds and leads a team spanning platform engineering, product management, and program operations, creating the infrastructure and governance frameworks that transform individual AI experiments into sustainable enterprise capabilities. Success requires balancing technical platform excellence with business storytelling, operational discipline with innovation agility, and centralized governance with distributed execution. This is the role that ensures Vantage’s AI investments compound rather than fragment, creating shared foundations that accelerate every team’s impact.
Essential Job Functions
- Build and lead the AI Platforms team spanning Halo platform and roadmap ownership, AI FinOps, go-to-market leadership, and program operations, developing capabilities that balance technical depth with business value communication.
- Own the Halo platform roadmap at the director level and drive the health, performance, and continuous improvement of Halo, establishing reliability standards, monitoring frameworks, and an onboarding experience that make the platform easy to access and use while ensuring stability and enabling innovation.
- Run Halo platform management, including support intake, bug triage, documentation and runbooks, and tiered support for the Halo platform and the AI applications built on it, including solutions delivered by the Accelerators team, as well as backlog prioritization, release and change management, and platform health reporting, along with builder onboarding that gets Business-Led Development teams the access, credentials, and development environment they need to start building.
- Build and lead Vantage’s AI FinOps practice, delivering visibility into metered AI consumption, including Copilot credits, development and operational tokens, and Azure spend, by user, department, and business unit, and partnering with Finance, FP&A, and IT on forecasting, budgeting, optimization, and the showback and chargeback model that prepares business units to own their AI costs in FY28.
- Drive enterprise-wide platform adoption through compelling value narratives, measurement frameworks, and user engagement strategies that demonstrate ROI and build organizational enthusiasm for AI-powered workflows.
- Establish and maintain the governance frameworks, reporting structures, and coordination mechanisms that enable the Navigators and Accelerators teams to operate effectively while maintaining organizational alignment.
- Partner with the Director, Navigators, Global to ensure platform capabilities are well-understood across functional areas, enabling Navigators to identify opportunities that leverage existing infrastructure rather than requiring net-new development.
- Partner with the Director, AI Delivery, Global to ensure platforms provide stable foundations for rapid prototyping, with clear integration patterns and development standards that accelerate solution delivery.
- Develop go-to-market strategies for AI capabilities that translate technical features into business value propositions, ensuring every AI solution ships with a clear narrative about why it matters to the organization.
- Oversee cross-cutting program operations including executive reporting, inter-team coordination, portfolio health tracking, and governance compliance that provide the Head of AI with decision-quality visibility into organizational performance.
Duties
- Recruit, onboard, and develop platform owners, adoption specialists, GTM leads, and program operations professionals, creating career pathways that retain high performers while building organizational capability.
- Establish platform health metrics, SLAs, and monitoring dashboards for Halo, ensuring reliability standards are met while identifying opportunities for performance optimization and feature enhancement.
- Design adoption measurement frameworks that go beyond usage counts to capture meaningful engagement, business impact, and user satisfaction metrics that demonstrate platform value to executive stakeholders.
- Create go-to-market playbooks that package AI capabilities with compelling narratives, training materials, and support resources that drive organic adoption across the enterprise.
- Build and maintain the program governance framework including portfolio tracking, resource allocation processes, decision gates, and executive reporting cadences that provide organizational transparency.
- Facilitate cross-team coordination between Navigators, Platforms, and Accelerators, ensuring information flows smoothly and dependencies are managed without creating bureaucratic overhead.
- Develop executive reporting packages that synthesize platform health, adoption metrics, delivery velocity, and business value realization into narratives that inform strategic decision-making.
- Architect platform evolution roadmaps that balance stability with innovation, incorporating feedback from platform users, Navigator discovery insights, and emerging technology capabilities.
- Improve the Halo builder experience by reducing time to first build, providing self-service access provisioning, and publishing golden paths and templates that remove common blockers such as permissions, credentials, and development toolchain setup.
- Lead the migration of existing applications into Halo, partnering with application owners and IT to plan, sequence, and support each migration.
- Build and maintain AI FinOps reporting dashboards and consumption guardrails, including per-user and per-department token and spend telemetry, budget alerts, and forecasts that flag overruns early and provide the reporting evidence required before any consumption reserve is drawn.
- Establish data governance, security, and compliance standards for platform operations that meet enterprise requirements while enabling the agility needed for rapid experimentation and prototyping.
- Partner with IT and Security on platform infrastructure, ensuring AI systems meet enterprise architecture standards, cybersecurity requirements, and operational resilience expectations.
- Monitor competitive landscape and industry trends in enterprise AI platforms, evaluating build versus buy decisions and identifying strategic partnership opportunities that extend platform capabilities.
- Design and facilitate platform community events, user groups, and feedback channels that build organizational engagement while surfacing improvement opportunities and feature requests.
- Manage vendor relationships for platform components, negotiating terms that preserve flexibility while ensuring support levels meet enterprise operational requirements.
- Contribute to AI strategy development by providing platform perspective on organizational capabilities, adoption readiness, and infrastructure investment priorities.
- Handle additional duties as assigned by Leadership.
Job Requirements
- Bachelor’s degree in Computer Science, Engineering, Business, or related field; advanced degree or relevant certifications highly desirable.
- Extensive experience leading platform or product teams in enterprise technology environments, with proven track record building and scaling technology platforms that achieved broad organizational adoption.
- Deep understanding of enterprise AI and cloud platforms, including Microsoft Azure and large language model (LLM) platforms, with hands-on experience managing platform health and evolution.
- Experience with FinOps or cloud cost management for consumption-based services, including usage telemetry, forecasting, budgeting, and showback or chargeback models; FinOps Foundation certification a plus.
- Experience running platform product management practices, including backlog management, bug triage, SLAs, release management, and developer experience for internal builders.
- Strong product management or go-to-market experience with ability to translate technical capabilities into compelling value propositions that resonate with non-technical business audiences.
- Demonstrated success building and leading diverse teams spanning technical platform ownership, analytics, product management, and program operations functions.
- Experience establishing governance frameworks, program management structures, and reporting cadences that provide organizational transparency without creating bureaucratic overhead.
- Exceptional stakeholder management capabilities with track record building trusted relationships across technical and business leadership in complex matrix organizations.
- Strong analytical orientation with experience designing metrics frameworks, adoption dashboards, and business value quantification approaches for technology platforms.
- Understanding of data center operations, infrastructure technology, or related industry contexts that enables credible engagement with business stakeholders about operational challenges.
- Experience with enterprise AI governance, responsible AI principles, and compliance requirements for AI platform deployment in regulated or mission-critical environments.
- Excellent communication skills with ability to present to executive leadership, facilitate cross-team coordination, and create compelling narratives about technology platform value.
- Track record of operating successfully in fast-paced environments where multiple priorities compete for attention and organizational structures evolve continuously.
- Collaborative leadership style with ability to influence across organizational boundaries, partner with peer directors, and maintain alignment without creating bottlenecks.
- Travel required is expected to be 15-25% for stakeholder engagement, platform community building, and cross-team coordination.
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: $200,000-210,000 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 is an Equal Opportunity Employer.
Vantage 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.
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 $200K-$210K 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 Vantage, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. Disclosed range: $200K to $210K.
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.
Vantage AI Hiring
Vantage has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $210K - $210K.
Location Context
AI roles in Denver pay a median of $200,000 across 134 tracked positions. That's 6% below 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 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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