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About This Role
Charlotte, NC
About Odyssey Logistics
Odyssey Logistics is a global logistics and supply chain partner helping businesses optimize performance through fully integrated, end\-to\-end solutions. We connect transportation, technology, and expertise across a broad network of services including 3PL and 4PL managed services, multimodal and intermodal transport, warehousing, trucking, Jones Act Ocean, and customs brokerage, ensuring goods move reliably, efficiently, and intelligently around the world.
Today, Odyssey supports more than 6,000 customers globally, delivering scalable, data driven solutions across complex supply chains. What started with a single client has grown into a dynamic, evolving organization focused on performance, precision, and long\-term partnership.
What Sets Us Apart:
Our work is complex, fast moving, and highly collaborative. Success here requires people who think strategically, operate with urgency, and navigate across functions to solve real business challenges. We value individuals who take ownership, challenge the status quo, and turn insight into action.
Our culture is grounded in five core values: Win Together, Innovate Boldly, Drive Results, Customer Centric, and Guide with Care. These are not just principles; they shape how we make decisions, partner with customers, and show up for each other.
At Odyssey, you are not just part of a company; you are part of building what comes next.
The Role:
Work Model: Hybrid
Work Schedule: Monday – Friday (Regular Business Hurs)
Travel Requirements: Yes – Less than 10% or as work demands
Supervisory Responsibilities: Yes
Reports to: Chief Information Officer
Pay Range: Competitive pay plus bonus plan
- All applicants must be currently authorized to work in the United States. No relocation allowance will be considered unless specifically addressed.
Summary / Primary Role:
As the Sr. Director of AI, you will own the direction and day\-to\-day execution of Odyssey's enterprise AI program. Building on a company\-wide AI Academy that has already put AI tools in the hands of nearly 140 people across every division, your mandate is to turn broad adoption into a focused set of proven platform wins and, ultimately, into workflows redesigned around AI. You will lead a cross\-divisional network of AI leads, partner closely with business unit leaders and the technology organization to identify and prioritize high\-value use cases and be accountable for governance and for measurable business value. Your technical depth in modern AI, your ability to translate between business and engineering, and your discipline in deciding what to scale will be instrumental to Odyssey's next chapter.
What You’ll Do:Leadership and Team Management:
- Lead and inspire a cross\-divisional network of AI leads, fostering a culture of collaboration, experimentation, and disciplined execution.
- Set the mandate, rhythm, and accountability for AI leads across operations, commercial, finance, and technology, helping them grow their AI fluency and impact.
- Build the long\-term internal AI capability program that currently relies on outside partners, by attracting and developing talent aligned with organization goals and values.
Technical Strategy and Execution:
- Set enterprise AI priorities across predictive, agentic, generative, and simulation use cases, aligning the roadmap with business objectives and saying no to the rest.
- Collaborate closely across business and IT functions, partnering with business unit leaders to identify use cases and with the technology organization to build them, delivering high\-quality solutions on time and within budget.
- Own the enterprise AI governance model – establish processes, sanctioned tools, data handling rules, and use\-case review \- balancing risk against speed and ensuring humans stay accountable for every AI\-assisted outcome.
Product Development and Innovation:
- Run a disciplined use\-case pipeline with robust criteria, converting the strongest ideas into proven platform wins with measurable business impact \- production, not pilots.
- Stay hands\-on with emerging AI models, tooling, and techniques, incorporating relevant innovations and pressure\-testing what is realistically buildable versus hype.
- Identify and start the highest\-value transformation opportunities \- workflows redesigned around AI, not just sped up by it \- treating the killing of a weak idea early as a health signal, not a failure.
Stakeholder Engagement and Communication:
- Collaborate closely with key stakeholders, including executives and business unit presidents, to gather requirements, prioritize initiatives, and communicate progress.
- Report progress and value in the language of business outcomes \- dollars and hours, not adoption percentages alone \- fostering transparency and alignment across the organization.
- Serve as a trusted advisor and subject matter expert on AI, translating technical reality into plain business language to inform decision\-making at every level.
What You Bring:
- Currently at Director level or above, with senior management experience
- Hands\-on experience architecting and scaling modern AI systems into production, ideally on a major cloud AI platform (e.g., AWS Bedrock, Azure AI Foundry, or Vertex AI)
- A track record of turning pilots into production platforms with measurable business impact and outcomes
- Proven collaboration across both business and IT functions \- equally credible with business unit leaders and with engineering teams, and able to move fluidly between the two.
- Program operating discipline \- experience running a real pipeline: intake, prioritization, triage, discard weak ideations early on, and tracking value through production.
- Strong communication skills \- able to translate technical reality into plain business language
- Demonstrated ability to lead through influence across a matrixed organization
- Experience in logistics, transportation, freight, or another asset\-heavy industry
- Experience standing up a new function from scratch; or experience building an AI or data governance model in a complex organization.
Benefits:
We offer a comprehensive and competitive compensation and benefits package, including:
- A choice of medical plans with FSA and HSA options
- Dental Insurance
- Vision Insurance
- Company\-paid Life and Disability Insurance
- 401(k) Plan with Company Match
- Paid Time Off (PTO) and Company Holidays
- Employee Assistance Program
- Company Health \& Wellness Program
- Discounts with Preferred Vendors
At Odyssey, we believe the best ideas come from people with different skills, experiences, and perspectives. We welcome applicants from all backgrounds and evaluate every candidate based on the qualifications needed for the role, while also valuing the unique strengths, curiosity, and fresh thinking each person brings. Our commitment to inclusion helps fuel innovation and enables us to deliver smarter, more creative solutions for our customers. We are proud to be a workplace where everyone is treated with respect, given equal opportunity to succeed, and encouraged to contribute in meaningful ways. All employment decisions are based on merit, without regard to race or ethnicity, religion, color, sex, gender identity, sexual orientation, age, disability, national origin, veteran status, or any other characteristic protected by federal, state, or local law.
Odyssey does not discriminate on the basis of actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth and pregnancy\-related conditions), gender identity or expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, genetic information, or any other characteristic protected by applicable federal, state or local laws and ordinances.
Visit us at: www.OdysseyLogistics.com/careers
Odyssey does not discriminate on the basis of actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth and pregnancy\-related conditions), gender identity or expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, genetic information, or any other characteristic protected by applicable federal, state or local laws and ordinances.
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 Odyssey Logistics & Technology Corporation, 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. Director-level AI roles across all categories have a median of $274,554.
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.
Odyssey Logistics & Technology Corporation AI Hiring
Odyssey Logistics & Technology Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, 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
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