Sr. AI/ML Engineer, RAIB

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.

About the Role

At McKesson, we are advancing healthcare through trusted, scalable, and responsible AI solutions. We are seeking a Sr. AI/ML Engineer, RAIB to lead the development, evaluation, governance, and operationalization of machine learning and Generative AI solutions. This role combines deep technical expertise in ML engineering with a strong focus on Responsible AI practices, model governance, AI risk management, explainability, and production-scale deployment.

You will partner with cross-functional stakeholders to build high-quality AI systems that are secure, reliable, explainable, and aligned with business and regulatory requirements while helping shape McKesson's AI engineering standards.

What You'll Do

● Lead the design, development, testing, and deployment of machine learning and Generative AI solutions using Python and modern ML frameworks.

● Review, validate, and challenge models developed by internal and external teams to ensure quality, performance, and risk compliance.

● Establish model evaluation frameworks for predictive models, LLMs, and GenAI applications.

● Conduct fairness, bias, and disparate impact assessments using tools such as Fairlearn and AIF360.

● Develop explainability and interpretability capabilities using SHAP, LIME, model cards, and documentation standards.

● Lead AI safety and GenAI evaluation activities, including red teaming, hallucination testing, toxicity assessment, and guardrail validation.

● Implement model monitoring, drift detection, observability, and AI risk controls within production environments.

● Partner with engineering, product, security, and governance teams to drive AI best practices, innovation, and operational excellence.

Basic Requirements

● 7+ years of experience designing, developing, deploying, and supporting production machine learning solutions.

● Bachelor's degree in computer science, Engineering, Data Science, Mathematics, or a related technical field, or equivalent experience.

● Experience with Python, as well as CI/CD and CLI/scripting tools

● Experience deploying classical machine learning models solving classification, regression, and time series forecasting problems

● Experience in managing ML infrastructure at scale in a cloud provider such as Azure (preferred) or AWS programmatically with infrastructure as code

● Hands-on experience with machine learning frameworks such as PyTorch and scikit-learn.

● Experience building and deploying ML models and data pipelines in production environments.

● Knowledge of model evaluation, validation, and performance measurement techniques.

● Demonstrated experience collaborating across technical and business teams to deliver complex AI initiatives.

Preferred Skills/Experience

● Experience evaluating and governing Large Language Models (LLMs) and Generative AI applications.

● Hands-on expertise with Fairlearn, AIF360, SHAP, and LIME.

● Experience with AI explainability, transparency, and model documentation practices.

● Knowledge of AI safety testing, red teaming, hallucination detection, toxicity monitoring, and guardrail implementation.

● Experience with model observability, drift detection, AI monitoring platforms, and MLOps tooling.

● Familiarity with Responsible AI frameworks and standards such as NIST AI RMF, ISO 42001, or comparable governance frameworks.

● Experience establishing AI risk management controls and audit-ready governance processes.

● Proven ability to mentor engineers, drive technical standards, and influence enterprise AI strategy.

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. 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. For more information regarding benefits at McKesson, please click here.

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