Sr. Machine Learning Ops Engineer

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.

Job Description:

As a ML OPS Engineer , you'll be part of a lean software team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. 

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support, build, and enable Machine Learning capabilities across the organization. You will work closely with internal customers and infrastructure teams to build our next generation data science workbench and ML platform and products. You will be able to further expand your knowledge and develop your expertise in modern Machine Learning frameworks, libraries and technologies while working closely with internal stakeholders to understand the evolving business needs. If you have a penchant for creative solutions and enjoy working in a hands-on, collaborative environment, then this role is for you.

What you’ll do in the role: 

• Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale.

• Deploy and manage machine learning & data pipelines in production environments.

• Work on containerization and orchestration solutions for model deployment.

• Participate in fast iteration cycles, adapting to evolving project requirements.

• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.

• Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.

• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.

• Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.

• Manage and monitor machine learning infrastructure, ensuring high availability and performance.

• Implement robust monitoring and logging solutions for tracking model performance and system health.

• Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.

• Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.

• Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.

• Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization.

• Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

Basic Qualifications:

• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.

• Typically requires 7+ years of hands-on work experience developing and applying advanced analytics solutions in a corporate environment with at least 4 years of experience programming with Python.

• At least 3 years of experience designing and building data-intensive solutions using distributed computing.