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### AI and Business Process Strategic Portfolio Integrator
- JR\-162135
- Hybrid
- Toronto
- Dallas
- Sales Enablement
- Full time
Who are we?
Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.
A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.
A career at Equinix means being at the center of shaping what comes next and amplifying customer value through innovation and impact. You’ll work across teams, influence key decisions, and help shape the path forward. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.Job Summary
The Strategic Portfolio Integrator is the connective layer across the CRO AI and Business Process Transformation portfolio. This role brings order and pace to a fast\-moving set of technology and AI\-enabled initiatives spanning marketing, sales, customer success, value engineering, and partner, ensuring every request is understood, prioritized against ROI, sequenced, and tracked through delivery. The Integrator acts as the single point of contact for CRO requests, translating raw asks into clear scope, and as a strategic advisor to the Senior Director of GTM AI Transformation and the Director of Business Process Excellence on where the team should focus next.
This is a business orchestration role, not a technical execution role. The Integrator ensures the portfolio reflects GTM strategy and delivers measurable revenue and productivity impact, working in close partnership with our Digital Innovation Office on technical delivery timelines without owning technical architecture.
Responsibilities
- Serve as single point of contact for CRO technology and AI\-related requests, quickly understanding intent at a high level and translating it into clear, actionable scope
- Stitch together the broader portfolio of technology and AI support requests into a coherent, prioritized view
- Support curation of business cases with defensible ROI tied to cost reduction, cost avoidance, or revenue growth, grounded in methodology rather than assumption
- Tie portfolio priorities back to GTM strategy, ensuring the work being resourced is the work that matters most
- Support process optimization work directly where extra capacity is needed across the team
- Recommend how incoming requests should be triaged, whether through AI optimization, process optimization, or tool optimization, and help focus the portfolio on the highest\-value requests
- Track all CRO requests across the prioritization and delivery pipeline, and communicate status clearly to executives and senior stakeholders
- Act as strategic advisor to the Senior Director of GTM AI Transformation and Director of Business Process Excellence on sequencing and prioritization
- Keep delivery on track and at pace across teams, surfacing risks and unblocking dependencies before they stall progress
Qualifications
- Experience in GTM Ops or RevOps, with working knowledge of end\-to\-end CRO processes across marketing, sales, technical sales, customer success, and partner organizations
- Familiarity with process optimization frameworks and a rigorous approach to business case development
- Comfort operating in a complex, highly matrixed environment with competing stakeholder priorities and urgent, high\-visibility requests
- Strong executive communication skills, with the ability to translate portfolio status into clear, confident updates for senior leaders
- A bias toward pace and follow\-through, able to keep multiple workstreams moving without losing sight of strategic intent
The targeted pay range for this position in the following location is / locations are:
United States \- Dallas Infomart Office DAI : 136,000 \- 204,000 USD / Annual
Canada \- Toronto Office TRO : 131,000 \- 181,000 CAD / Annual
Our pay ranges reflect the minimum and maximum target for new hire pay for the full\-time position determined by role, level, and location.The pay range shown is based on our compensation structure in place at the time of posting and may be updated periodically based on business needs. Individual pay is based on additional factors including job\-related skills, experience, and relevant education and/or training.
The targeted pay range listed reflects the base pay only and does not include bonus, equity, or benefits. Employees are eligible for bonus, and equity may be offered depending on the position.
Equinix Benefits
As an employee, you become important to Equinix’s success. We ensure all your benefits are in line with our core values: competitive, inclusive, sustainable, connected and efficient. We keep them competitive within the current marketplace to ensure we’re providing you with the best package possible. So, wherever you are in your career and life, you’ll be able to enhance your experience and bring your whole self to work.
Employee Assistance Program: An Employee Assistance program is available to all employees.
US Benefits: \- Insurance: You may enroll in health, life, disability and voluntary plans that are designed for you and your eligible family members. \- Retirement: You and Equinix may contribute to a retirement plan to help you plan for your financial future. \- Paid Time Off (PTO) and Paid Holidays: You will receive an accrued amount of PTO each pay period along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to change and may be subject to specific plan or program terms. Canada Core Benefits: \- Insurance: You may enroll in healthcare coverage that is designed to complement the provincial healthcare system, along with life, disability and optional benefit plans that are designed for you and your eligible family members. \- Retirement: You may also enroll in Equinix\-sponsored retirement or savings plans: Defined Contribution Pension Plan (DCPP), Group Retirement Savings Plan (RRSP) and Tax\-Free Savings Plan (TSFA). \- Vacation and Paid Holidays: Equinix offers both vacation and personal time, along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to specific plan or program terms, and to change at Equinix discretion.
Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing form.
Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
We use artificial intelligence in our hiring process. Learn more here.
This posting is for a backfill position, meaning it is to fill an existing vacancy within our organization.
Salary Context
This $131K-$204K range is below the median 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 Equinix, 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 $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 ($167K) sits 23% below the category median. Disclosed range: $131K to $204K.
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.
Equinix AI Hiring
Equinix has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Dallas, TX, US, Chicago, IL, US. Compensation range: $204K - $403K.
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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