AI/ML Cloud Engineer

Position Description

Are you an experienced and innovative Cloud Engineer with a passion for MLOps? McKesson invites you to be part of our dynamic team, where your expertise will be critical in developing and enhancing our MLOps platform strategy. Ensure our machine learning operations are secure, robust, scalable, and efficient.

Key Roles & Responsibilities :

Master Terraform and DevOps Practices : Harness the power of Terraform to construct and manage cloud infrastructure, ensuring seamless and scalable environments for machine learning operations. Utilize DevOps methodologies to enhance our MLOps platform, fostering automation and efficiency.

Azure Cloud Technology Expertise : Showcase a deep understanding of Azure cloud services and architecture. Leverage Azure resources (e.g., Azure Machine Learning, Azure Databricks) to seamlessly integrate components of the MLOps infrastructure.

Automation : Develop and implement automation scripts and workflows using Azure DevOps or similar tools to streamline MLOps platform management tasks, enhance efficiency, and minimize errors. Design, implement, and manage Azure DevOps pipelines to support seamless CI / CD, boosting efficiency and deployment speed for machine learning models.

Problem Solving : Diagnose and resolve complex technical issues related to the MLOps platform, including performance bottlenecks, data processing challenges, and model deployment issues. Utilize Azure tools (e.g., Azure Monitor, Azure Log Analytics) for effective troubleshooting.

Cost Optimization : Monitor platform usage and costs, identifying opportunities for optimization and cost-saving measures. Implement strategies to optimize resource utilization and reduce unnecessary expenses within the MLOps environment.

Platform Management : Oversee the day-to-day operations of the Machine Learning platform, ensuring optimal performance, security, and scalability. This includes model lifecycle management, workload optimization, and anomaly monitoring.

Security and Compliance : Ensure that the MLOps environment adheres to security best practices and complies with relevant regulations. Implement security measures such as access controls, encryption, and vulnerability management.

Collaboration : Work closely with data scientists, machine learning engineers, and other stakeholders to provide technical guidance and support for their projects. Facilitate the efficient use of the MLOps platform and address their specific needs.

Continuous Improvement : Stay updated with the latest Azure features and best practices, recommending and implementing platform enhancements to improve performance, scalability, and security.

Minimum Requirements :

7 years of relevant experience.

Bachelor’s degree or equivalent experience.

Critical Skills & Experience :

Strong programming skills in a modern language like Python, with experience in machine learning frameworks.

Solid understanding of Azure DevOps and Azure Cloud Services.

Extensive experience in software engineering / development, with a proven track record of implementing large-scale, complex systems.

Deep understanding of design patterns and principles, and coding standards with best practices for building scalable, resilient, and secure applications.

Expert knowledge in cloud-native architectures, preferably Azure, including experience with services such as Azure Virtual Machines, Azure Functions, and Azure Kubernetes Service.

Advanced experience with JIRA / Confluence and GITHUB, Jenkins, GitLab CI, or Azure DevOps.

Deep understanding of Infrastructure as Code for provisioning and configuration management across the MLOps architecture.

Experience with SQL and NoSQL databases.

Experience with installing, deploying, configuring, recommending improvements, and maintaining the Azure Docker / Kubernetes cluster itself through deployment pipelines and automation.

Experience in agile development methodology (e.g. Scrum, Kanban).

Excellent communication, interpersonal, and leadership skills, with the ability to influence and build consensus among stakeholders.

Preferred Skills :

Experience designing and building large-scale MLOps applications.

Experience with service mesh technologies such as Istio.

Familiarity with data streaming platforms like Kafka and event-driven architectures.

Track record of leading successful digital transformations or cloud migrations.

At McKesson, we are committed to leveraging cutting-edge technology to transform healthcare solutions. If you are passionate about MLOps, cloud engineering, and innovation, and thrive in a collaborative environment, join us on this exciting journey. Your expertise will be at the core of our mission to revolutionize machine learning operations.

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

Our Base Pay Range for this position

150,500 - $250,900

Salary : $150,500 - $250,900