Interested in this AI/ML Engineer role at McKesson?
Apply Now →About This Role
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.
What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you.
The internal McKesson Technology team is at the forefront of driving digital transformation, including the adoption and integration of artificial intelligence (AI) to deliver measurable value across the organisation.
We are seeking a Principal AI Transformation Strategist to accelerate the company's adoption of advanced AI technologies and ensure value realisation at enterprise scale. This individual contributor role operates as a senior-most expert, advising executives, shaping strategy, and guiding complex AI programmes across multiple business units. You will serve as a trusted thought partner to senior leadership, influencing long-range roadmaps and ensuring AI initiatives deliver measurable business impact. This role partners closely with Product, Data Science, Engineering, Operations, Compliance, and business leaders and reports to the VP, IT AI Innovation & Automation.
Key Responsibilities
- Enterprise AI strategy leadership with demonstrated ability to translate corporate objectives into a multi-year, value-backed AI portfolio.
- Deep expertise across AI/ML architecture, model lifecycle (from data readiness to deployment and monitoring), and operations in regulated environments.
- Proven executive influence: frames complex technical topics into clear business narratives that drive prioritisation and funding decisions.
- Hands-on familiarity with MLOps/ModelOps platforms, data quality controls, model risk management, and responsible AI practices.
- Portfolio governance: establishes standards, reusable frameworks, and decision guardrails that scale across business units.
- Ability to design and review KPIs/OKRs that track adoption, time-to-value, reliability, safety, and total cost of ownership.
- Strong vendor/platform evaluation skills and the judgement to balance build vs. buy, speed vs. control, and innovation vs. risk.
- Exceptional written and verbal communication skills; comfortable presenting to VP/SVP and enterprise steering committees.
Programme Delivery
- Define and maintain the enterprise AI portfolio roadmap, sequencing initiatives to maximise compound value and reduce duplicated effort.
- Shape scope and success criteria for high-impact programmes; coach delivery leaders while holding teams accountable for outcomes.
- Remove structural blockers (operating model, data access, funding mechanisms) to accelerate scaled delivery.
Performance Tracking
- Define benefits realisation plans with clear baselines and target metrics (e.g., productivity, quality, cycle time, revenue uplift).
- Establish standard dashboards for executive consumption; ensure telemetry is reliable, auditable, and explains variance.
- Lead quarterly value reviews and recommend portfolio rebalancing based on performance and risk.
Stakeholder Engagement
- Serve as a senior advisor to product and engineering leaders on shaping roadmaps for scale, auditability, and measurable outcomes.
- Represent McKesson Technology in executive steering committees and enterprise governance forums.
- Influence without authority across BU leaders, Compliance, Risk, Security, and Legal to align on standards and guardrails.
Enterprise Governance & Risk
- Co-lead the definition and rollout of Responsible AI, model risk, and policy controls; ensure controls are effective yet developer-friendly.
- Chair or materially contribute to model review boards; serve as escalation point for high-risk AI deployments.
Technology Strategy & Architecture
- Evaluate and recommend technical approaches, tools, and platforms that improve reliability, safety, and time-to-value.
- Guide reference architectures for generative and predictive AI; ensure patterns are secure, observable, and cost-aware.
External Thought Leadership
- Represent McKesson in strategic partner engagements, technology councils, and cross-industry working groups.
- Continuously scan the market and regulatory landscape and translate implications into pragmatic internal guidance.
Minimum Requirement
Degree or equivalent and typically requires 13+ years, with 8+ years, of direct work-related experience. Less years required if has relevant Master's or Doctorate qualifications.
Critical Skills
- 12-15+ years in AI, advanced analytics, data engineering, or technology transformation, including 7+ years guiding large-scale programmes.
- Demonstrated success taking AI solutions from concept to scaled production in complex, multi-stakeholder enterprises.
- Experience in regulated industries with comfort navigating compliance, risk, audit, and privacy requirements.
- Advanced degree in a quantitative or technical field (e.g., Computer Science, Engineering, Data Science) preferred.
- Relevant certifications (e.g., cloud, security, agile, or programme management) are a plus.
Behavioural Competencies
- Executive Presence: Communicates with clarity and credibility; simplifies complexity for decision makers.
- Systems Thinking: Anticipates second- and third-order effects; optimises for the whole rather than local maxima.
- Accountability: Owns outcomes and sets a high bar for pace, quality, and integrity.
- Collaboration: Builds trust and effective working rhythms across Product, Data, Engineering, and Operations.
- Adaptability: Thrives amid ambiguity and evolving technology; adjusts course based on signal and evidence.
- Pragmatic Innovation: Balances ambition with feasibility to ship value responsibly.
Why This Role Is Critical
This Principal role shapes how McKesson responsibly applies AI at enterprise scale - driving measurable outcomes for patients, providers, and partners. By setting strategy, establishing guardrails, and accelerating delivery, you will materially influence the success, safety, and sustainability of AI across the company.
At McKesson, we care about the well-being of the patients and communities we serve, and that starts with caring for our people. That’s why we have a Total Rewards package that includes comprehensive benefits to supportphysical, mental, and financial well-being. Our Total Rewards offerings serve the different needs of our diverse employee population and ensure they are the healthiest versions of themselves.
As part of Total Rewards, we are proud to offer a competitive compensation package at McKesson. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations.In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered.
Our Base Pay Range for this position
€105,000 - €175,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson’s (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind:
McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.
McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates.
McKesson job postings are posted on our career site: careers.mckesson.com.
Benefits
*
Coverage you can rely on
+ Medical, Dental, and Vision + Health Spending Accounts + Flexible Spending Accounts *
Benefits that go beyond your base pay
+ 401(k) (U.S.) + Pension (Canada) + Employee Stock Purchase Plan *
Support for total well-being
+ Mental Health Programs + Flexible Schedules + Paid Time Off + Wellness Program + Education Reimbursement + Volunteer Opportunities + Flexible Work Environment *
A culture powered by belonging
By fostering a culture of belonging, we advance our goal of being the best place to work in healthcare—connecting the right talent to the right roles to solve our most critical challenges.
Salary Context
This $105K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At McKesson, 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $105K to $175K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
McKesson AI Hiring
McKesson has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Munster, IN, US, Columbus, OH. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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