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About This Role
Owens \& Minor is a global healthcare solutions company providing essential products, services and technology solutions that support care delivery in leading hospitals, health systems and research centers around the world. For over 140 years, Owens \& Minor has delivered comfort and confidence behind the scenes, so healthcare stays at the forefront, helping to make each day better for the hospitals, healthcare partners, and communities we serve. Powered by more than 14,000 teammates worldwide, Owens \& Minor exists because every day, everywhere, Life Takes Care™.
Global Reach with a Local Touch
- 140\+ years serving healthcare
- Over 14,000 teammates worldwide
- Serving healthcare partners in 80 countries
- Manufacturing facilities in the U.S., Honduras, Mexico, Thailand and Ireland
- 40\+ distribution centers
- Portfolio of 300 propriety and branded product offerings
- 1,000 branded medical product suppliers
- 4,000 healthcare partners served
Benefits
- Comprehensive Healthcare Plan \- Medical, dental, and vision plans start on day one of employment for full\-time teammates.
- Educational Assistance \- We offer educational assistance to all eligible teammates enrolled in an approved, accredited collegiate program.
- Employer\-Paid Life Insurance and Disability \- We offer employer\-paid life insurance and disability coverage.
- Voluntary Supplemental Programs – We offer additional options to secure your financial future including supplemental life, hospitalization, critical illness, and other insurance programs.
- Support for your Growing Family – Adoption assistance, fertility benefits (in medical plan) and parental leave are available for teammates planning for a family.
- Health Savings Account (HSA) and 401(k) \- We offer these voluntary financial programs to help teammates prepare for their future, as well as other voluntary benefits.
- Paid Leave \- In addition to sick days and short\-term leave, we offer holidays, vacation days, personal days, and additional types of leave – including parental leave.
- Well\-Being – Also included in our offering is a Teammate Assistance Program (TAP), Calm Health, Cancer Resources Services, and discount programs – all at no cost to you.
- The anticipated salary range for this position is $180,000 USD Annual or more. The actual compensation offered may vary based on job related factors such as experience, skills, education and location
The AI Solutions Architect is a senior hybrid business\-and\-technology role responsible for identifying, designing, governing and scaling AI\-enabled solutions across the enterprise. The role combines business solution architecture, AI platform architecture and responsible AI governance into one accountable function.
This architect partners with business leaders to identify high\-value AI opportunities and works closely with IAM, Cybersecurity, Infrastructure/Cloud, Enterprise Architecture, Data \& Analytics and Enterprise Applications teams to ensure each AI solution is feasible, secure, compliant, supportable and aligned to enterprise standards. This role is fully remote. Applicants are welcome to apply anywhere within the United States.
Role Mission
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- Turn Claude Enterprise and related AI capabilities into measurable business outcomes, not just platform adoption.
- Create a governed intake\-to\-delivery path for AI use cases across business functions.
- Translate business opportunities into practical AI designs that technical teams can implement safely.
- Ensure AI solutions have proper access controls, security review, data governance, operational readiness and responsible AI oversight.
- Scale repeatable AI patterns across Supply Chain, Procurement, Operations, Finance, Sales, Customer Service and IT.
Key Responsibilities
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AI Business Value \& Use Case Portfolio
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- Facilitate business ideation workshops and discover high\-value AI opportunities.
- Prioritize AI use cases based on business value, feasibility, adoption readiness, risk and strategic alignment.
- Develop business cases, define KPIs and track value realization after deployment.
- Maintain an enterprise AI use case roadmap for Claude Enterprise and related governed AI capabilities.
Business Solution Design
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- Translate business needs into future\-state AI\-enabled workflows, user journeys and operating\-model impacts.
- Define human\-in\-the\-loop checkpoints, data needs, exception handling and adoption requirements.
- Create solution briefs that are understandable to business stakeholders and actionable for technical teams.
- Partner with product, process and functional leaders to move from experimentation to governed adoption.
Technical Architecture \& Platform Enablement
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- Define Claude Enterprise usage patterns, solution templates and approved architecture approaches.
- Support MCP integration pattern design for enterprise systems, APIs and approved data sources.
- Assess integration complexity, data dependencies, technical feasibility and operational readiness.
- Coordinate with Infrastructure/Cloud, Enterprise Architecture, Data Engineering and application teams to ensure designs are scalable and supportable.
Governance, Responsible AI \& Compliance
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- Prepare use cases for AI governance review and maintain required documentation.
- Coordinate risk classification, policy alignment, human oversight, auditability and exception management.
- Ensure business teams understand approved, restricted and prohibited AI usage patterns.
- Report on adoption, value realization, risk posture and control maturity.
IAM, Cybersecurity \& Control Partnership
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- Work closely with IAM on RBAC, SSO, provisioning, access reviews and privileged access considerations.
- Work closely with Cybersecurity on data protection, audit logging, vendor risk, prompt/data handling and security architecture review.
- Ensure IAM and Security remain accountable for their domains while this role validates that AI solutions align with their controls.
- Escalate unresolved control gaps before production deployment or scaled adoption.
Adoption, Change \& Value Realization
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- Create AI playbooks, business adoption guides, use case templates and champion enablement materials.
- Track active usage, stakeholder satisfaction, productivity benefits, cycle\-time reduction and lessons learned.
- Promote reuse of proven AI patterns across business functions.
- Help leaders establish practical, responsible and measurable AI adoption habits.
Required Qualifications
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- 8\+ years of experience across business transformation, solution architecture, enterprise applications, cloud/platform architecture, digital transformation or related roles.
- Practical knowledge of GenAI platforms, Claude Enterprise, Platform, API and its usage patterns, LLM capabilities, agentic workflows and MCP\-style integration concepts.
- Strong business acumen with ability to translate operating challenges into AI\-enabled outcomes, business cases and measurable KPIs.
- Sufficient technical depth to lead design conversations with IAM, Cybersecurity, Infrastructure, Data Engineering and Enterprise Applications teams.
- Working knowledge of API integration, enterprise architecture, identity/access controls, security review processes, data governance and operational readiness practices.
- Strong facilitation and executive communication skills across business leaders, governance committees, architects, engineers and vendor partners.
Preferred Qualifications
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- Experience standing up AI governance, AI adoption programs, LLMOps or enterprise AI operating models.
- Experience with Supply Chain, Procurement, Operations, Distribution, Healthcare, Finance, Sales or Customer Service use cases.
- Experience creating reusable solution patterns, playbooks, adoption guides, scoring frameworks or intake processes.
- Familiarity with tools and platforms such as SAP, Salesforce, ServiceNow, Snowflake, Smartsheet, API gateways or workflow automation platforms.
Required Education Qualifications
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. Masters Degree or PHD in Computer Engineering/Data Science
If you feel this opportunity could be the next step in your career, we encourage you to apply. This position will accept applications on an ongoing basis.
Owens \& Minor is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, national origin, sex, sexual orientation, genetic information, religion, disability, age, status as a veteran, or any other status prohibited by applicable national, federal, state or local law.
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 Owens & Minor, 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.
Owens & Minor AI Hiring
Owens & Minor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US.
Location Context
AI roles in Los Angeles pay a median of $214,112 across 708 tracked positions.
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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