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About This Role
Job Summary
The Senior AI Engineer is a hands-on AI, data, analytics, and automation practitioner embedded within a business domain such as Commercial, Operations, Product, or Corporate Functions. The role identifies, designs, prototypes, and delivers practical solutions that drive measurable business value.
- The Senior AI Engineer works directly with business teams to understand workflows, decisions, pain points, and opportunities, then rapidly translates them into working proofs of concept, pilots, demonstrations, automations, agents, copilots, analytics solutions, and decision-support tools.
This is a hands-on builder role.
- The Senior AI Engineer is expected to use modern AI-assisted development tools and approved enterprise platforms to create solutions directly, not simply define requirements or coordinate delivery. The role owns outcomes, not tickets.
- The Senior AI Engineer selects the simplest effective approach. A solution may use process improvement, traditional workflow automation or RPA, analytics, machine learning, generative AI, or a combination of these approaches. The role determines what can be delivered directly and what should transition to engineering, data science, platform, product, or automation teams for production deployment and scale.
- The role serves as the bridge between business stakeholders and technical teams, combining strong communication, business understanding, product thinking, technical problem solving, and hands-on execution. The Senior AI Engineer remains engaged through testing, transition, adoption, and measurable value realization.
Job Description
Trusted Partnership & Opportunity Identification
- Embed within a business domain and develop a deep understanding of its processes, workflows, systems, decisions, handoffs, and operational challenges.
- Build trusted relationships with business leaders and frontline users; serve as a primary partner for AI-, data-, analytics-, and automation-enabled problem solving.
- Continuously identify opportunities to improve decisions, productivity, customer experience, quality, cycle time, or business performance.
- Shape and manage demand, focusing teams on opportunities with the highest potential value, feasibility, and readiness.
- Challenge existing ways of working and help teams envision materially different workflows, not only incremental improvements.
Solution Design & Rapid Prototyping
- Lead hands-on discovery sessions to define the business problem, user need, current workflow, constraints, baseline performance, and success measures.
- Translate ambiguous business problems into clear solution hypotheses, experiments, and technical requirements.
- Determine whether the best response is process improvement, workflow automation, RPA, analytics, machine learning, generative AI, or a combination of approaches.
- Design AI-enabled workflows, agents, copilots, retrieval-augmented generation solutions, analytics and decision-support tools, workflow automations, and process automations.
- Build working proofs of concept, prototypes, pilots, demonstrations, and minimum viable solutions using real data and approved enterprise platforms.
- Test directly with users, incorporate feedback rapidly, and assess business value, usability, technical feasibility, controls, and scalability.
- Make practical tradeoffs among speed, user value, technical complexity, and enterprise requirements.
Hands-On AI, Analytics & Automation Delivery
- Personally build functional solutions when the scope and technology are appropriate, including AI applications, agents, copilots, RAG solutions, analytics tools, workflow automations, RPA solutions, integrations, and decision-support tools.
- Use Python, SQL, APIs, low-code tools, automation platforms, and AI-assisted coding capabilities to move quickly from an idea to a usable solution.
- Work across enterprise data, APIs, business applications, AI platforms, analytics platforms, and workflow technologies to turn ideas into working outcomes.
- Apply sound engineering practices for solution design, testing, documentation, security, reliability, maintainability, and secure data handling.
- Define evaluation criteria with the business owner and incorporate testing, explainability, monitoring, controls, and human review as appropriate.
- Troubleshoot technical and workflow issues during prototyping, piloting, implementation, and transition.
- Ensure solutions align with enterprise architecture, cybersecurity, data governance, privacy, and Responsible AI requirements.
- Maintain a bias for working solutions and measurable outcomes, not presentations or intermediate artifacts.
Scale, Transition & Business Impact
- Determine which solutions can be owned and supported directly and which require transition to engineering, data science, platform, product, automation, or business technology teams.
- Partner with technical teams to harden, productionize, integrate, deploy, scale, and support successful solutions.
- Provide clear technical requirements, implementation guidance, documentation, and knowledge transfer when transitioning execution or long-term ownership.
- Remain accountable for continuity of the solution and intended business outcome through handoff, deployment, adoption, and scale.
- Drive user adoption, measure realized value against an agreed baseline, and refine solutions using user feedback and operational metrics.
- Build and maintain a prioritized domain opportunity roadmap.
- Create reusable components, prompts, agent patterns, automation templates, connectors, evaluation approaches, accelerators, and implementation patterns that help future teams deliver faster.
MINIMUM JOB REQUIREMENTS
Education
- Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Statistics, Analytics, or a related field, or equivalent practical experience.
Certification / Licensure
- No certification or licensure required.
Work Experience
- 5+ years of experience in AI/ML engineering, data science, data engineering, analytics engineering, automation engineering, solution engineering, or a related technical discipline.
- Demonstrated experience building and delivering AI, analytics, data, or automation solutions that address real business problems and are used in business workflows.
- Experience personally creating proofs of concept, prototypes, pilots, demonstrations, or minimum viable solutions.
- Experience working directly with business stakeholders and end users to translate ambiguous needs into practical solutions.
- Experience moving solutions from discovery and prototype through pilot, deployment, or transition to a production delivery team.
Knowledge / Skills / Abilities
- Strong hands-on Python and SQL skills.
- Ability to independently build AI applications, workflow automations, RPA solutions, analytics solutions, data workflows, integrations, APIs, and decision-support tools.
- Working knowledge of generative AI, large language models, prompt engineering, retrieval-augmented generation, agents, model evaluation, APIs, data integration, and workflow automation.
- Solid machine learning, statistics, experimentation, forecasting, and optimization fundamentals.
- Working knowledge of version control, testing, CI/CD, monitoring, documentation, and secure data handling.
- Ability to rapidly learn unfamiliar business domains, workflows, systems, data, and technology platforms.
- Strong product mindset with a focus on user needs, adoption, measurable value, and iterative delivery.
- Ability to communicate clearly with technical and non-technical audiences and influence across organizational boundaries.
- Ability to balance rapid experimentation with enterprise architecture, security, data governance, privacy, and Responsible AI requirements.
- Sound judgment on when to build directly, when to use low-code or RPA, and when to transition execution to specialized teams.
- Bias for action, hands-on problem solving, and ownership from idea through outcome.
PREFERRED JOB REQUIREMENTS
Education
- Master's degree in Computer Science, Engineering, Data Science, Analytics, Business Administration, or a related field.
Certification / Licensure
- Relevant cloud, AI, data, analytics, automation, agile, or product certifications preferred.
Work Experience
- Experience building generative AI applications, agents, copilots, RAG solutions, intelligent workflow automations, RPA solutions, analytics products, forecasting models, optimization tools, or decision-support solutions.
- Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, Microsoft Fabric, Databricks, Azure Machine Learning, Power Platform, Power Automate, Copilot Studio, or comparable enterprise platforms.
- Experience in forward deployed engineering, solution engineering, technical consulting, analytics engineering, automation engineering, or another business-facing technical role.
- Experience integrating enterprise systems, APIs, data platforms, analytics platforms, automation tools, and business applications.
- Experience in healthcare, medical products, supply chain, manufacturing, distribution, commercial operations, or corporate functions.
Knowledge / Skills / Abilities
- Ability to move fluidly between business discovery, solution design, coding, user testing, demonstration, and implementation planning.
- Strong facilitation and storytelling skills; able to lead working sessions, demonstrate solutions, explain technical concepts simply, and drive decisions.
- Comfort working in environments with imperfect data, evolving processes, and incomplete requirements.
- Demonstrated ability to deliver results in fast-paced, collaborative, and execution-oriented organizations.
- Equally comfortable engaging business stakeholders, building working solutions, and partnering with technical teams to productionize and scale them.
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:
$154,000.00 - $231,000.00 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 $154K-$231K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($192K) sits 9% below the category median. Disclosed range: $154K to $231K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Medline Industries AI Hiring
Medline Industries has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Northbrook, IL, US. Compensation range: $231K - $231K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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