Interested in this AI/ML Engineer role at Walgreens?
Apply Now →About This Role
Job Summary:
Designs, develops, and implements methods, processes and systems to consolidate and analyze diverse data sets including structured and unstructured. Manages the daily operational activities that develop and implement tactical plans for specified area. Oversees team(s) assigned to projects and may manage implementations. Ensures resources are accurately allocated in line with tactical plans and appropriately accounted for.
Job Responsibilities:* Acts as subject matter expert and advisor on topic of the largest most complex analytical tools, enterprise data systems and/or analytical methodologies for the company.
- Develops and ensures proper execution by other team members of the most complex innovative data visual presentation strategies to ensure data is conveyed in meaningful and interpretive formats for all audience levels.
- Develops and ensures proper execution by other team members of the most complex large scale innovative analytical and/or statistical methodologies and supporting tools for evaluating Company processes, business problems, and/or ROI of products and services.
- Answers largest most complex questions from the field. Manages the distribution of field support work among the pricing analysts to ensure all issues are resolved in a timely manner. Serves as industry leader and subject matter expert in one or more areas for most complex methodologies/models.
- Creates and oversees the training schedule of new employees as well as participate in a significant amount of training for new and existing employees. Provides analytical guidance, mentoring, and coaching to the team and peers.
- Develops and maintains an automated process to identify data and process integrity issues. Provides leadership with detailed recommendation for issue resolution. Identifies both up and down stream impact of identified issues and fully communicates with any impacted parties.
- Develops timely, innovative, objective analysis which leads to fact-based solutions for strategic business issues. Identifies, formulates, and drives problem definition and hypothesis formulation as it relates to the specific business environment. Synthesizes externally syndicated data with internal experience. Prepares standard and ad hoc management reports and presentations for more complex projects. Utilizes query based tools to create and run queries used for data mining and analytics to support other job responsibilities and ad hoc projects.
- Leads the technical development; ensures proper execution of most complex models and tools used to compare executed contract terms and conditions to parameters included in internal and external data sources. Identifies data source deficiencies among company data and suggests path to resolve.
- Reviews and approves methodologies identified in the compliance monitoring program to include evaluation of definitions, price increase maintenance and network identification.
- Reviews and approves improvements to standard model assumptions and multifaceted business problems within the organization. Updates processes, using findings from performance monitoring (For example, adding order logic steps to improve on data matching).
- Works closely with the business users and IT resources, consulting and understanding their needs, establishing realistic expectations, developing analytical work plans around problems, owning the development and delivery of work plans and then delivering back to the client. Provides consulting services related to data analysis, analytic tools, and enterprise analytic architecture.
- Works cross functionally across divisions with key stakeholders
- Responsible for meeting all company directives, contractual or otherwise, as it relates to assigned client portfolio and delegating supporting functions to direct reports as appropriate.
About Walgreens
Founded in 1901, Walgreens (www.walgreens.com) has a storied heritage of caring for communities for generations, and proudly serves nearly 9 million customers and patients each day across its approximately 8,500 stores throughout the U.S. and Puerto Rico, and leading omni-channel platforms. Walgreens has approximately 220,000 team members, including nearly 90,000 healthcare service providers, and is committed to being the first choice for retail pharmacy and health services, building trusted relationships that create healthier futures for customers, patients, team members and communities.
Basic Qualifications
- Bachelor's degree and at least 2 years of experience in Finance, Economics, Accounting, Math, Business, Actuarial Science, or Computer Science OR High School Diploma / GED and at least 5 years of experience in Finance, Economics, Accounting, Math, Business, Actuarial Science, or Computer Science.
- Experience with Pharmacy Industry related business practices, data and pricing methodologies.
- Experience establishing and maintaining key relationships with internal (peers, business partners and leadership) and external (business community, clients and vendors) within a matrix organization to develop strategies that meet department goals within budget and timelines.
- Experience developing ways of accomplishing goals with no supervision, depending upon oneself and proper delegation to resolve issues to ensure completed objectives and determining when escalation of issues is necessary.
- Experience using time management skills such as prioritizing/organizing and tracking details and meeting deadlines of multiple projects with varying completion dates.
- Experience in diagnosing, isolating, and resolving complex business issues and recommending and implementing strategies to resolve problems.
- Experience overseeing the performance of direct or indirect reports and being held accountable for their deliverables.
- Advanced level skill in Microsoft Excel (for example: using AVERAGE function, merging and centering cells, printing centered page and/or creating a pivot table).
- Advanced level skill in Microsoft PowerPoint (for example: changing picture styles, customizing animation and/or setting automatic slide timings).
- Advanced level skill in Microsoft Word (for example: adding developer tab, creating a macro, formatting table style, inserting comments and/or creating a mail merge).
- Advanced level skill in Microsoft Access
- At least 2 years of experience contributing to financial decisions in the workplace.
- At least 2 years of direct leadership, indirect leadership and or cross functional team leadership
- Willing to travel up to 15% of the time for business purposes (within state and out of state).
Preferred Qualifications
- + Graduate degree in a quantitative, computational or technical discipline
+ Experience interacting at the executive level
+ Experience leading cross-functional teams.
We will consider employment of qualified applicants with arrest and conviction records. The Salary below is being provided to promote pay transparency and equal employment opportunities at Walgreens. The actual hourly salary within this range that you will be offered will depend on a variety of factors including geography, skills and abilities, education, experience and other relevant factors. This role will remain open until filled. To review benefits, please click here jobs.walgreens.com/benefits. If you are applying on a job board or unable to click on the link, please copy and paste this URL into your browser jobs.walgreens.com/benefits
Salary Range: $100000 - $175000 / Salaried
Salary Context
This $100K-$175K range is in the lower quartile 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 Walgreens, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $100K to $175K.
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.
Walgreens AI Hiring
Walgreens has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Deerfield, IL, US. Compensation range: $175K - $175K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.