CONFIDENTIAL Sr. Specialist, Machine Learning Engineering

Mckesson Corporation; Cork; Hybrid; EUR 72,000 - 120,000; Full time

Job summary

McKesson seeks a Sr. Specialist, Machine Learning Engineering to design, build, and scale production-grade AI, ML, and generative AI solutions that deliver meaningful business impact.

You will lead scalable ML platforms, production pipelines, and model deployment frameworks while partnering with cross-functional teams. You will provide technical leadership across the full AI lifecycle—from experimentation and model development through deployment, observability, governance, and continuous

Qualifications

• 7+ years of experience designing, building, and supporting machine learning solutions in enterprise environments.
• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related field, or equivalent experience.
• Strong software engineering experience with Python and modern application development practices.
• Experience deploying machine learning models into production cloud environments.
• Hands-on experience with MLOps including CI/CD pipelines, model lifecycle management, automation, and monitoring.
• Experience with Azure Machine Learning, Azure Databricks, or comparable cloud-based ML platforms.
• Experience with containerization technologies such as Docker and production deployment patterns.

Responsibilities

• Lead the design, development, deployment, and operation of enterprise-scale AI/ML and LLM solutions.
• Build and productionize machine learning pipelines using Azure Machine Learning, Azure Databricks, and cloud-native services.
• Establish and enhance MLOps and LLMOps practices including CI/CD, model versioning, automated testing, monitoring, and governance.
• Develop scalable deployment architectures using containerization, infrastructure as code, and automation frameworks.
• Partner with data scientists and product teams to transition prototypes into reliable production systems.
• Implement model observability, performance monitoring, drift detection, and reliability engineering practices.
• Lead technical troubleshooting, root cause analysis, and continuous improvement efforts for AI-enabled platforms.
• Mentor engineers and promote engineering excellence through documentation, knowledge sharing, and best practices.

Skills

Python

CI/CD

MLOps

Azure ML

Databricks

LLMs

Kubernetes

Docker

Terraform

AI governance

Education

Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related field

Tools

Azure ML

Azure Databricks

Docker

Kubernetes

Terraform

Job description

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

McKesson is seeking a Sr. Specialist, Machine Learning Engineering to design, build, and scale production-grade AI, machine learning, and generative AI solutions that deliver meaningful business impact. In this role, you will lead the development of scalable ML platforms, production pipelines, model deployment frameworks, and MLOps capabilities while partnering with cross-functional teams to solve complex business and technology challenges.

You will provide technical leadership across the full AI lifecycle, from experimentation and model development through deployment, observability, governance, and continuous improvement.

What You'll Do

• Lead the design, development, deployment, and operation of enterprise-scale AI/ML and LLM solutions.

• Build and productionize machine learning pipelines using Azure Machine Learning, Azure Databricks, and cloud-native services.

• Establish and enhance MLOps and LLMOps practices including CI/CD, model versioning, automated testing, monitoring, and governance.

• Develop scalable deployment architectures using containerization, infrastructure as code, and automation frameworks.

• Partner with data scientists and product teams to transition prototypes into reliable production systems.

• Implement model observability, performance monitoring, drift detection, and reliability engineering practices.

• Lead technical troubleshooting, root cause analysis, and continuous improvement efforts for AI-enabled platforms.

• Mentor engineers and promote engineering excellence through documentation, knowledge sharing, and best practices.

Basic Requirements

• 7+ years of experience designing, building, and supporting machine learning solutions in enterprise environments.

• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience.

• Strong software engineering experience with Python and modern application development practices.

• Experience deploying machine learning models into production cloud environments.

• Hands-on experience with MLOps including CI/CD pipelines, model lifecycle management, automation, and monitoring.

• Experience with Azure Machine Learning, Azure Databricks, or comparable cloud-based machine learning platforms.

• Experience with containerization technologies such as Docker and production deployment patterns.

• Ability to communicate technical concepts effectively to both technical and business stakeholders.

Preferred Skills/Experience

• Experience developing and deploying Generative AI and Large Language Model (LLM) solutions.

• Experience with Azure AI Foundry, prompt engineering, evaluation frameworks, and AI guardrails.

• Knowledge of LLMOps practices, RAG architectures, model evaluation, and AI observability.

• Experience with Terraform or other Infrastructure-as-Code tools.

• Experience with Kubernetes, AKS, distributed computing, or large-scale data processing platforms.

• Familiarity with AI governance, responsible AI, security, privacy, and compliance practices.

• Experience in healthcare, supply chain, pharmaceutical, or distribution industries.

• Prior experience mentoring engineers and leading complex, cross-functional initiatives.

Travel / Work Environment / Physical Requirements

• Travel may be required based on business needs.

• Position may be remote, hybrid, or office-based depending on organizational requirements.

• Must be able to perform standard computer-based work activities and communicate effectively with internal and external stakeholders.

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 support physical, 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

€72,000 - €120,000

McKesson job postings are posted on our career site: careers.mckesson.com