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Apply Now →About This Role
Who we are:Samsonite is the worldwide leader in superior travel bags, luggage, and accessories combining notable style with the latest design technology and the utmost attention to quality and durability. For more than 100 years, Samsonite has leveraged its rich heritage to create unparalleled products that fulfill the travel lifestyle needs of conscious movers everywhere. With a portfolio of brands including Samsonite, Tumi, American Tourister, High Sierra, Gregory, Hartmann, and Lipault Paris, our products are sold in over 100 countries in North America, Asia, Europe and Latin America through our company\-operated retail store, websites and a variety of retail partners.
At Samsonite Group, we are building the next generation of AI\-enabled capabilities to elevate consumer experience, sharpen operations, and scale innovation across a global, multi\-brand portfolio. We are seeking a Director of AI to help shape and accelerate this transformation \- a role at the intersection of applied AI, enterprise platform delivery, and global business transformation, translating enterprise strategy into shipped outcomes.
Reporting to the CIO, the Director is accountable for execution and measurable business impact. A successful candidate will partner closely with the regional IT leaders, business function heads, and AI champions to ensure consistent delivery across our multi\-brand, multi\-region portfolio. This is not a traditional engineering role and not a purely strategic role \- it is a high\-leverage delivery leadership role for someone who can identify the right problems, shape AI\-enabled solutions, drive pilots, measure value, and help scale what works.
AI Portfolio Delivery \& Value Realization
- Execute the enterprise AI roadmap across tablestakes, transformational, and disruptive opportunity tiers; own end\-to\-end delivery from prototype through production.
- Drive rapid experimentation cycles and accelerate time\-to\-value, ensuring disciplined progression from pilot to scaled deployment without bottlenecks.
- Own definition, tracking, and reporting of ROI and business KPIs; tie initiatives directly to revenue growth, margin improvement, cost efficiency, or working capital optimization.
Maintain a disciplined backlog of AI use cases across consumer experience, supply chain, retail and eCommerce, marketing, finance, and operations.
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AI Delivery \& Platform Integration
- Lead the development, deployment, and scaling of AI/ML and GenAI solutions, including consumer\-facing experiences (conversational assistants, agentic commerce, personalization) and AI\-driven marketing and discovery capabilities.
- Work with Digital and Data Strategy team to lay solid foundation for Samsonite’s AI journey
- Drive integration of AI capabilities into core enterprise platforms (eCommerce, CRM, CDP, SAP, O365\) and establish scalable MLOps practices for lifecycle management, monitoring, and cost/performance optimization.
Operate the AI technology stack day\-to\-day and drive strategic platform and ecosystem decisions in partnership with IT leadership (hyperscalers, GenAI platforms, agentic commerce platforms, enterprise SaaS).
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AI Governance \& Compliance Execution
- Operate the enterprise AI governance framework; ensure compliance with EU AI Act, GDPR, SOX/GRC, and equivalent regional regulations, with support from regional teams.
- Maintain audit\-ready model documentation, risk assessments, and validation evidence in coordination with Legal, Compliance, and Internal Audit.
Apply readiness tests before promoting AI from pilot to production, particularly for autonomous and agentic deployments; flag governance gaps to the CIO.
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Cross\-Regional Coordination
- Partner with regional IT and business leaders across Americas, EMEA, and APAC to deliver AI use cases tuned to local market and regulatory needs.
Coordinate with regional AI roles and adoption leads to ensure consistent execution; scale proven regional use cases group\-wide.
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AI Adoption \& Enablement
- Build, lead, and develop the global AI CoE; create reusable patterns, accelerators, and documentation to compound delivery velocity.
- Operate enterprise AI literacy and upskilling programs (tiered by user proficiency) and a network of AI champions across regions and functions, in partnership with HR.
Lead group\-wide activation and adoption of enterprise AI tooling (copilots, GenAI assistants, productivity AI platforms) and maintain a shared inventory of approved tools.
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Vendor \& Budget Execution
- Manage day\-to\-day relationships with AI vendors, hyperscalers, and delivery partners; execute against the approved AI budget and surface variance and reallocation needs.
Support build\-vs\-buy assessments with delivery feasibility, effort estimates, and operating cost modeling.
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Stakeholder Reporting
- Provide regular delivery updates to the CIO and prepare materials for executive and Board reporting; present to leadership as needed.
- Translate technical progress into clear business outcomes for non\-technical stakeholders across functions and regions.
Why you'll love working here:* Our employees matter. As a people\-focused business, we work hard to provide meaningful rewards and development opportunities for our employees, recognizing performance, and creating a supportive working environment for them, wherever they are based.
- Vibrant culture. We are committed to a diverse and rich culture, welcoming people from all walks of life. Our long\-standing commitment to culture and inclusion empowers us to bring our authentic selves and unique differences to work every day.
- Socially responsible. We want to minimize our products' impact on the environment and help create positive journeys worldwide. We do this by creating the best products using the most sustainable and innovative materials, methods, and models.
What we value:At Samsonite, we do more than create the bags that move with our consumer, we inspire and celebrate the moments that move them. We believe we have a responsibility to the world in how we operate, the products we sell, the communities where we live and work, and how we treat the people we employ. We're as diverse as travel itself, and like travel, your journey with Samsonite presents the opportunity to be a part of something bigger and explore your passions. This is why we offer various paths for professionals and celebrate the knowledge and skills they bring to our team. We are committed to a respectful workplace that allows our team members to bring their best selves to the workplace daily.
Samsonite is an equal opportunity employer and is committed to promoting and maintaining a work environment in which all applicants, associates, customers, and other individuals are treated with dignity and respect free from unlawful harassment, discrimination, or retaliation.
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 Samsonite, 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. Director-level AI roles across all categories have a median of $272,150.
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.
Samsonite AI Hiring
Samsonite has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mansfield, MA, 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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