Interested in this AI/ML Engineer role at Vertiv?
Apply Now →Skills & Technologies
About This Role
Job Summary
As a Global Engineering AI Solutions Manager , the role will l ead the design, implementation, and support of AI\-driven solutions within the Oracle SCM ecosystem to automate processes, improve operational efficiency, and enable data\-driven decision\-making across the product lifecycle. The position focuses on leveraging agentic AI systems that integrate large language models (LLMs), enterprise data, APIs, and orchestration frameworks to deliver secure, scalable, and context\-aware solutions.
The role will ensure alignment between AI capabilities and Product Lifecycle Management (PLM) processes, including product data governance, engineering change management, cross\-functional collaboration, and supply chain integration. Experience building AI agents, particularly with exposure to Oracle Product Development Cloud , is considered an added advantage.
Responsibilities:
- Lead the design, configuration, and deployment of AI\-driven agents that can reason, plan, and execute multi\-step tasks to support product lifecycle and supply chain processes.
- Develop and manage LLM\-based prompt orchestration and agent workflows to enhance automation, decision support, and knowledge access across PLM operations.
- Oversee the implementation and continuous improvement of Oracle Cloud solutions , leveraging AI\-driven automation to streamline product development, engineering change, and product data management processes.
- Ensure seamless integration of AI capabilities with Oracle SCM, PLM modules, and third\-party enterprise applications to enable end\-to\-end digital product lifecycle connectivity.
- Configure and optimize AI agent behaviors using Oracle\-native tools, workflows, and APIs , ensuring alignment with enterprise PLM standards and governance.
- Translate complex business and product lifecycle requirements into scalable, AI\-enabled solutions that improve operational efficiency and product data visibility.
- Monitor, optimize, and troubleshoot AI agent performance , ensuring reliability, accuracy, and continuous improvement of AI\-enabled processes.
- Establish and enforce data governance, security, and compliance frameworks for AI implementations across global PLM systems.
- Collaborate closely with engineering, product development, supply chain, IT, and business stakeholders to drive adoption of AI\-enabled PLM capabilities.
- Provide technical documentation, knowledge transfer, and operational support to ensure sustainable deployment and adoption of AI\-driven solutions.
- Implement Retrieval\-Augmented Generation (RAG) frameworks leveraging structured and unstructured enterprise data to enable intelligent search, insights, and decision support within PLM and Oracle SCM environments.
Requirements :
- Bachelor’s degree in information technology, Process Management, or a related field, or equivalent experience
- 15\+ years of experience working with Oracle Fusion and enterprise systems, including hands\-on exposure to integrations and customizations using REST APIs and related integration frameworks.
- Exposure to Oracle AI Agent Studio or similar enterprise AI platforms for designing and managing AI\-enabled workflows.
- Ability to conceptualize and guide the development of AI agent frameworks such as supervisor, sequential, and workflow\-based agents to support enterprise automation.
- Strong understanding of enterprise Generative AI technologies and Large Language Models (LLMs) and their application within business and product lifecycle management processes.
- Experience applying prompt engineering techniques for structured enterprise use cases, including grounding AI responses with enterprise data, and implementing Retrieval\-Augmented Generation (RAG) frameworks.
- Practical understanding of LLM limitations, including hallucination risks, with the ability to establish mitigation strategies and validation controls.
- Solid knowledge of enterprise AI security, governance, and compliance standards to ensure responsible and controlled AI adoption.
- Experience defining role\-based access controls, approval workflows, and human\-in\-the\-loop governance models for AI\-enabled processes.
- Familiarity with data privacy regulations, access management, and regulatory compliance considerations in AI\-enabled enterprise systems.
- Proven experience in integrating enterprise applications with internal and external systems using REST APIs, SOAP services, and other enterprise integration technologies.
- Business \& Organizational Knowledge: Demonstrates strong understanding of current and emerging business practices, industry trends, technologies, and organizational priorities to effectively support strategic objectives.
- Communication Skills: Exhibits excellent written, verbal, and listening skills, with the ability to clearly communicate with team members, cross\-functional stakeholders, and leadership.
- Initiative \& Ownership: Proactively takes ownership of responsibilities, drives initiatives independently when needed, and embraces new challenges and opportunities for improvement.
- Judgment \& Decision\-Making: Applies analytical thinking, professional experience, and sound judgment to make effective and timely decisions that support business goals.
- Change Management \& Adaptability: Effectively manages and adapts to changing priorities, business needs, and strategic direction within a dynamic and globally distributed organization.
- Professionalism \& Integrity: Demonstrates a high standard of professionalism, accountability, and ethical conduct in all aspects of work and interactions.
- Collaboration \& Team Leadership: Encourages teamwork and works collaboratively across departments and functions to achieve shared objectives and deliver business value.
- Results Orientation: Maintains a strong focus on performance, accountability, and delivery of measurable results while consistently meeting or exceeding defined objectives.
*The successful candidate will embrace Vertiv’s Core Principals \& Behaviors to help execute our Strategic Priorities.*
OUR CORE PRINCIPLES : Safety. Integrity. Respect. Teamwork. Inclusion.
OUR STRATEGIC PRIORITIES
- High\-Performance Culture
- Customer Focus
- Operational Excellence
- Innovation
- Financial Strength
VERTIV BEHAVIORS
- Own it
- Act with urgency
- Foster a customer\-first mindset
- Think big and execute
- Lead by example
- Drive continuous improvement
- Learn and seek out development
Promote transparent \& open communication
*
*About Vertiv*
Vertiv (NYSE: VRT) brings together hardware, software, analytics and ongoing services to enable its customers’ vital applications to run continuously, perform optimally and grow with their business needs. Vertiv solves the most important challenges facing today’s data centers, communication networks and commercial and industrial facilities with a portfolio of power, cooling and IT infrastructure solutions and services that extend from the cloud to the edge of the network. Headquartered in Westerville, Ohio, USA, Vertiv employs around 34,000 people and does business in more than 130 countries. Visit Vertiv.com to learn more.
*Equal Opportunity Employer*
Vertiv is an Equal Opportunity/Affirmative Action employer. We promote equal opportunities for all with respect to hiring, terms of employment, mobility, training, compensation, and occupational health, without discrimination as to age, race, color, religion, creed, sex, pregnancy status (including childbirth, breastfeeding, or related medical conditions), marital status, sexual orientation, gender identity / expression (including transgender status or sexual stereotypes), genetic information, citizenship status, national origin, protected veteran status, political affiliation, or disability. If you have a disability and are having difficulty accessing or using this website to apply for a position, you can request help by sending an email to [email protected].
*\#LI\-RB1*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vertiv, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vertiv AI Hiring
Vertiv has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westerville, OH, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.