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Job Summary
As the Industry Verticals Product Principal – AI, Cloud, Data Center you will lead high-impact product initiatives for B2B vertical solutions, driving strategic decisions and innovation. You will partner with customers across various industry verticals to define, build, and launch highly differentiated solutions. Leveraging deep expertise in product strategy, customer insights, and market analysis, you will shape initiatives that drive share, profitability, and industry leadership. This role requires work experience in the AI/DC and/or IT and Technology space with a deep infrastructure knowledge.
Essential Functions
- Strategic Thinking & Vision: Shape and drive a compelling product vision and strategy that aligns with enterprise goals and addresses critical B2B / B2C market needs.
- Product Lifecycle Management: Lead the entire product lifecycle from ideation and validation to Go-To-Market & Commercialization and end-of-life.
- Market & Competitive Analysis: Serve as an advocate for industry-specific needs in all product development efforts by conducting customer research, analyzing trends, and gathering insights.
- Cross-Functional Orchestration: Lead initiatives with engineering, design, operations, and marketing teams to achieve high-yielding results and drive alignment across the organization.
- Data & Analytics: Leverage analytics and customer feedback to guide strategy, measure impact, and optimize solutions, establishing KPIs to track progress.
- Communication & Influence: Serve as a thought leader in industry verticals product / solutions innovation, representing the company externally and internally and presenting opportunities to senior leadership.
- Lead Technology Strategy: Translate complex technical solutions into scalable, business-aligned outcomes.
- Drive Solutions, GTS & GTM Execution: Define value propositions, messaging, and end-to-end strategies for market adoption.
- Support Customer Solutions: Assess needs, identify gaps, and recommend optimized approaches to meet business and technical requirements.
- Lead Program Execution: Oversee cross-functional initiatives and manage global programs from strategy through deployment.
- Develop Business Cases: Build ROI-driven frameworks to support investment decisions and customer value articulation.
- Enable AI & Automation Adoption: Implement intelligent workflows to scale marketing, sales, and customer engagement.
- Leverage Data & Insights: Apply data-driven segmentation and targeting to enhance decision-making and personalization.
- Strengthen Partner Ecosystems: Collaborate with strategic partners to develop joint solutions and expand market reach.
Minimum Education
Bachelor’s degree in Business, Marketing, Supply Chain, Engineering, Computer Science, Management Information Systems, or related field or equivalent directly related experience.
Minimum Experience
10+ years in industry verticals product & solutions management, innovation, or related roles with increasing leadership responsibility. An advanced degree may substitute for experience.
Knowledge, Skills, and Abilities:
- Strong industry verticals experience with expertise in building and launching products.
- Proven success in full product lifecycle management, from ideation to launch.
- Skilled in data and analytics, metrics-driven decision-making, and customer insights.
- Exceptional leadership, communication, influence, and stakeholder engagement skills.
- Experience leading cross-functional orchestration for complex initiatives.
- Expertise of Data centers and Cloud ecosystems, excel at translating complex, high-availability infrastructure into compelling value propositions, strategic marketing plans, and recommendations, while also possessing the technical depth to guide customers and the business.
- AI, Cloud or Data Center infrastructure experience in either the end-to-end orchestrator, product marketing, consultive sales support or technical implementation experience.
- Logistics experience in this vertical a plus.
Preferred Qualifications:
Pay Transparency:
Pay: FEC_E30
USA: $10,589.64/mo - $24,003.18/mo, CO: $10,589.64/mo - $23,003.05/mo, CA: $11,177.95/mo - $19,061.35/mo, NJ: $11,177.95/mo - $17,884.72/mo, ME, OH & VT: $11,177.95/mo - $20,002.65/mo, MN: $11,177.95/mo - $22,002.91/mo, IL & NV: $11,177.95/mo - $23,003.05/mo, MD, NY, VA & WA: $11,177.95/mo - $24,003.18/mo, MA: $11,766.26/mo - $24,003.18/mo, RI: $12,942.89/mo - $22,002.91/mo, CT: $12,942.89/mo - $23,003.05/mo, DC & HI: $13,531.20/mo - $23,003.05/mo, NYC: $13,531.20/mo - $24,003.18/mo
Additional Details: Application Criteria: Upload current copy of Resume (Microsoft Word or PDF format only) and answer job screening questionnaire by Thursday, Oct 8
Domicile and Work Model: The preferred location for this hybrid position is Memphis, TN. However, we will consider candidates based elsewhere within the United States, excluding Alaska, Hawaii, and U.S. territories. Candidates residing within 50 miles of a FedEx campus will be required to work on-site at a FedEx location several times per week.
Pay Transparency:
The compensation listed reflects the pay range or rate of pay reasonably expected for this posted position at the posted location or locations. If this opportunity includes multiple job levels, the pay information represents the ranges for each level in that job family. Actual pay is determined by several job-related factors permitted by law and relevant to the position, including, but not limited to, experience relative to the job, tenure, market level, pay at the location for this job, performance, schedule, and work assignment. In California, the compensation listed reflects the range or rate of pay reasonably expected for this posted position upon hire. In New Jersey, any compensable Security and Walk time will be paid to non-exempt/hourly employees at the state minimum wage.
For details on our comprehensive benefits, click here.
Federal Express Corporation is an Equal Opportunity Employer including, Vets/Disability.
Reasonable accommodations are available for qualified individuals with disabilities throughout the application process. Applicants who require reasonable accommodations in the application or hiring process should contact [email protected].
Applicants have rights under Federal Employment Laws:
- Know Your Rights
- Pay Transparency
- Family and Medical Leave Act (FMLA)
- Employee Polygraph Protection Act
E-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E-Verify program:
- E-Verify Notice (bilingual)
- Right to Work Notice (English) / (Spanish)
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At FedEx, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
FedEx AI Hiring
FedEx has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Collierville, TN, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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