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About This Role
Job Summary
The Head of Data & AI Platforms leads the engineering organization responsible for building and operating the foundation that enables AI to transform healthcare at Medline. This role owns the enterprise data and AI platforms that power how the company generates, scales, and operationalizes insight and intelligence.
This is a deeply technical leadership role focused on taking an already strong data platform to the next level—evolving it into a unified, production-grade Data + AI platform that supports modern AI, including machine learning, generative AI, and emerging agentic patterns at enterprise scale. The role ensures that every AI and data product at Medline is built on a reliable, scalable, and reusable foundation—driving speed, quality, and consistency across the enterprise. Job Description
MAJOR RESPONSIBILITIES:
Platform Ownership
- Own the enterprise Data and AI Platforms, including the roadmap, architecture, and engineering best practices.
- Evolve Medline’s existing data platform into a unified Data + AI platform that supports ML, GenAI, and emerging AI application patterns.
- Establish the platform as a shared, reusable foundation that scales across all domains and use cases.
Modernize and extend the platform to support real-time data, large-scale model inference, and next-generation AI workloads. *
Data, AI & MLOps Engineering Leadership
- Lead Data Engineering, ML/AI Platform (MLOps), and AI Platform engineering as a single, high-performing organization.
Build and scale a world-class engineering team with strong depth in distributed systems, data platforms, and production AI. *
Standards, Quality & Production Readiness
- Define and enforce engineering standards, platform guardrails, and production readiness criteria.
- Ensure the platform is secure, reliable, and cost-efficient by design.
Establish SLAs and operational excellence practices to run the platform as a high-availability service.
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Operating Model & Ways of Working
- Design the platform to be the fastest and easiest path for teams to build and deploy data and AI products.
- Drive adoption through strong developer experience, tooling, and self-service capabilities.
Establish clear onboarding pathways and support models for enterprise teams.
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Outcomes & Operational Excellence
- Improve time-to-production for AI and data products across the enterprise.
- Increase the number and scale of models and data products running reliably in production.
Continuously optimize platform cost, performance, and developer productivity.
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Platform Run / Operations
- Manage teams that monitor the platform and be accountable for monitoring, service levels on availability, and incident management
- Establish SLAs, availability standards, monitoring requirements, and operational governance
Provide executive oversight of major incidents, coordinated recovery, and corrective actions that reduce recurring issues and drive resolutions through automation, self-service and knowledge management. *
MINIMUM JOB REQUIREMENT S
Education
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
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Certification / Licensure
No specific certification or licensure required.
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Work Experience
- 15+ years of experience in engineering leadership across data platforms, distributed systems, or AI/ML infrastructure.
Proven track record of building and scaling enterprise-grade data or AI platforms. *
Knowledge / Skills / Abilities
- Deep hands-on understanding of modern cloud data platforms (Databricks, Microsoft Fabric, or equivalent).
- Strong experience with MLOps and production AI systems (model serving, feature stores, monitoring, lifecycle management).
- Demonstrated ability to lead engineering teams and deliver highly scalable, reliable platforms.
- Strong platform-as-a-product mindset — shared services, SLAs, and reuse over rebuild.
Strong bias for outcomes — platform uptime, cost, and time-to-production over artifacts. *
PREFERRED JOB REQUIREMENTS
Education
Master’s degree in Computer Science, Engineering, or a related technical field.
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Certification / Licensure
Relevant cloud, data platform (Databricks / Azure), or MLOps credentials.
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Work Experience
- Prior experience leading large remote / India / offshore engineering teams.
Experience delivering an enterprise-wide Data + AI platform from the ground up. *
Knowledge / Skills / Abilities
- Experience evolving an existing data platform into a modern Data + AI platform.
- Familiarity with GenAI, LLM platforms, and emerging agentic architectures.
- Experience operating shared platforms with SLAs and internal customer models.
- Experience leading global engineering teams, including offshore models.
- Strong track record of improving engineering velocity and platform adoption at scale.
Familiarity with Azure Data Factory, Azure Data Lake, Databricks, Azure Synapse, Microsoft Fabric and/or Power BI is preferred. *
DISCLAIMER
All duties and requirements are subject to possible modification to reasonably accommodate individuals with disabilities.
This position description in no way states or implies that these are the only duties to be performed by an employee occupying this position. Employees will be required to follow any other job-related instructions and to perform any other job-related duties requested by their supervisor(s)/manager(s).
This document does not create an employment contract, implied or otherwise, other than an "at will" employment relationship.
Click here to learn more about a career in IT at Medline!
Medline Industries, LP, and its subsidiaries, offer a competitive total rewards package, continuing education & training, and tremendous potential with a growing worldwide organization.
The anticipated salary range for this position:
$203,000.00 - $305,000.00 Annual
The actual salary will vary based on applicant’s location, education, experience, skills, and abilities. Medline will not pay less than the applicable minimum wage or salary threshold.
Our benefit package includes health insurance, life and disability, 401(k) contributions, paid time off, etc., for employees working 30 or more hours per week on average. For roles where employees work less than 30 hours per week, benefits include 401(k) contributions as well as access to the Employee Assistance Program, Employee Resource Groups and the Employee Service Corp.
We’re dedicated to creating a Medline where everyone feels they belong and can grow their career. We strive to do this by seeking diversity in all forms, acting inclusively, and ensuring that people have tools and resources to perform at their best. Explore our Belonging page here .
Medline Industries, LP is an equal opportunity employer. Medline evaluates qualified individuals without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, age, disability, neurodivergence, protected veteran status, marital or family status, caregiver responsibilities, genetic information, or any other characteristic protected by applicable federal, state, or local laws.
Salary Context
This $203K-$305K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Medline Industries, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($254K) sits 20% above the category median. Disclosed range: $203K to $305K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Medline Industries AI Hiring
Medline Industries has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Northbrook, IL, US. Compensation range: $231K - $305K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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