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About This Role
Logistics at full potential.
At GXO, we’re constantly looking for talented individuals at all levels who can deliver the caliber of service our company requires. You know that a positive work environment creates happy employees, which boosts productivity and dedication. On our team, you’ll have the support to excel at work and the resources to build a career you can be proud of.
Principal Cloud AI Platform Architect
Are you ready to take your career to the next level with a rapidly growing global company? As the Principal Cloud AI Platform Architect, Google Cloud, you will serve as GXO's principal authority for Google Cloud architecture, platform engineering, and enterprise cloud design. This role provides hands\-on technical leadership across Google Cloud Platform (GCP), establishing architectural standards, engineering best practices, and secure\-by\-default cloud foundations that enable enterprise\-scale innovation.
If you're looking for an opportunity to shape the future of cloud architecture and enterprise AI at a global scale, join us at GXO.
Pay, benefits and more
We are eager to attract the best, so we offer competitive compensation and a generous benefits package, including full health insurance (medical, dental and vision), 401(k), life insurance, disability and more.
What you'll do on a typical day
- Provide principal\-level architecture leadership for GXO's Google Cloud environment, including landing zones, organization policies, networking, identity, security, and platform design.
- Design and evolve enterprise Google Cloud foundations including folder and project hierarchy, Shared VPC, Private Service Connect, Identity\-Aware Proxy (IAP), Workload Identity Federation, Secret Manager, Cloud KMS/CMEK, and Cloud Logging and Monitoring.
- Lead architecture for Google Kubernetes Engine (GKE), including networking, ingress/egress, autoscaling, security, observability, production operations, and deployment standards.
- Establish enterprise platform engineering standards through Terraform modules, GitOps, CI/CD pipelines, DevSecOps controls, policy\-as\-code, and paved\-road developer experiences.
- Develop and maintain enterprise reference architectures, architectural decision records (ADRs), technical standards, and cloud design patterns.
- Partner with Information Security to implement zero\-trust architecture, identity federation, least privilege access, encryption, secrets management, audit logging, and secure\-by\-default cloud operations.
- Define enterprise observability and FinOps standards including cost attribution, budgeting, service level objectives (SLOs), tracing, monitoring, and operational excellence.
- Lead the Google Cloud architecture for GXO's Enterprise AI Platform, including Gemini Enterprise Agent Platform, LiteLLM, Agent Gateway, MCP, Agent Registry, ADK, and governed Snowflake integrations.
- Partner with Data Engineering, Security, and Product Architecture to design secure, scalable, model\-agnostic AI inference, agent runtimes, identity passthrough, tool execution, and end\-to\-end traceability.
- Contribute to future AI platform capabilities including model tiering, open\-source model serving on GKE, and evolving enterprise AI architecture.
- Serve as the principal Google Cloud architect supporting additional enterprise workloads including analytics, application modernization, integration, and data platforms.
- Evaluate emerging Google Cloud technologies and strategic partner offerings for enterprise adoption.
- Conduct architecture reviews, design governance, technical mentorship, and executive\-level technology recommendations.
- Collaborate with enterprise, infrastructure, security, and data architects to establish consistent cloud architecture standards across GXO.
- Provide hands\-on technical leadership by writing Terraform, reviewing IAM policies, troubleshooting GKE networking, and partnering directly with engineering teams.
What you need to succeed at GXO
At a minimum, you'll need
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical field, or equivalent work experience.
- Google Cloud Professional Cloud Architect certification.
- 12–15\+ years of experience in cloud architecture, enterprise architecture, distributed systems, or platform engineering.
- 7\+ years of hands\-on experience designing and operating enterprise\-scale Google Cloud environments.
- Deep expertise in Google Cloud architecture including landing zones, organization policies, IAM, Workload Identity Federation, networking, Shared VPC, Private Service Connect, VPC Service Controls, Cloud KMS/CMEK, IAP, logging, monitoring, and secure cloud operations.
- Extensive experience designing and operating production Google Kubernetes Engine (GKE) platforms.
- Strong experience implementing Infrastructure as Code using Terraform, GitOps, CI/CD pipelines, and policy\-as\-code frameworks.
- Demonstrated expertise implementing zero\-trust cloud security, identity federation, least privilege access, encryption, secrets management, audit logging, and governance.
- Experience designing enterprise observability and FinOps capabilities including monitoring, tracing, cost management, budgeting, and operational maturity.
- Experience designing enterprise AI or LLM platforms utilizing Vertex AI, Gemini Enterprise Agent Platform, LiteLLM, MCP, AI gateways, agent runtimes, or comparable technologies.
- Excellent analytical skills with the ability to translate complex business requirements into scalable enterprise architectures.
- Strong written communication skills with experience producing executive\-ready architecture documentation, standards, design reviews, and technical recommendations.
- Exceptional communication and collaboration skills with the ability to engage engineering teams, executive leadership, technology partners, and security organizations.
- Proven ability to prioritize competing initiatives while balancing architecture strategy, hands\-on engineering, and stakeholder engagement.
- Ability to operate effectively with minimal supervision in a fast\-paced, global enterprise environment.
It'd be great if you also have
- Google Cloud Professional Security Engineer certification.
- Google Cloud Professional DevOps Engineer certification.
- Google Cloud Professional Machine Learning Engineer certification.
- Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD).
- HashiCorp Terraform Associate certification.
- Experience integrating Google Cloud workloads with Snowflake using OAuth, RBAC, masking policies, row\-level security, and governed data access.
- Experience designing secure multi\-tenant enterprise platforms on Google Cloud.
- Experience evaluating emerging AI, cloud, and platform technologies for enterprise adoption.
- Demonstrated success collaborating within enterprise architecture organizations while contributing to shared standards and technical governance.
- Experience mentoring architects and engineers while elevating enterprise architecture practices across large organizations.
We engineer faster, smarter, leaner supply chains
GXO is a leading provider of cutting\-edge supply chain solutions to the most successful companies in the world. We help our customers manage their goods most efficiently using our technology and services. Our greatest strength is our global team – energetic, innovative people of all experience levels and talents who make GXO a great place to work.
We are proud to be an Equal Opportunity employer including Disabled/Veterans.
GXO adheres to CDC, OSHA and state and local requirements regarding COVID safety. All employees and visitors are expected to comply with GXO policies which are in place to safeguard our employees and customers.
All applicants who receive a conditional offer of employment may be required to take and pass a pre\-employment drug test.
The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. All employees may be required to perform duties outside of their normal responsibilities from time to time, as needed. Review GXO's candidate privacy statement here.
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 GXO Logistics, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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.
GXO Logistics AI Hiring
GXO Logistics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in NC, US.
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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