Do you want to be part of an inclusive team that works to develop innovative therapies for patients? Every day, we are driven to develop and deliver innovative and effective new medicines to patients and physicians.  If you want to be part of this exciting work, you belong at Astellas!

Astellas Pharma Inc. is a pharmaceutical company conducting business in more than 70 countries around the world. We are committed to turning innovative science into medical solutions that bring value and hope to patients and their families. Keeping our focus on addressing unmet medical needs and conducting our business with ethics and integrity enables us to improve the health of people throughout the world. For more information on Astellas, please visit our website at www.astellas.com.

Purpose:

As an MLOps Engineer, you will play a crucial role in developing and maintaining the infrastructure and tools that enable our machine learning initiatives. You will collaborate closely with engineers, data scientists, analysts, and business stakeholders to identify challenges, define project scopes, and implement effective solutions.

Your primary focus will be on enhancing productivity across the engineering team by removing impediments and advocating for modern development practices. You will mentor team members on best practices and scalable development techniques, ensuring a balance between product development and individual skill growth.

Key responsibilities include automating processes through CI/CD pipelines, scaling up machine learning inference, and leading the implementation of innovative solutions. This role offers the opportunity to work with cutting-edge technologies, including the latest advancements in artificial intelligence and generative AI, positioning you at the forefront of industry developments.

 

Essential Job Responsibilities:

Design and Implement Infrastructure: Develop infrastructure solutions using Infrastructure as Code (IaC) practices to support machine learning workflows. Develop and Manage CI/CD Pipelines: Streamline development and deployment processes by creating efficient CI/CD pipelines. Automation and Scripting: Create, maintain, and optimize automation scripts for testing, deployment, and monitoring of machine learning systems. Cross-functional Collaboration: Work closely with application development, project management, operations, and security teams to resolve technical challenges and ensure successful project execution. Apply Best Practices: Incorporate software engineering best practices, including automation and continuous integration/deployment, into machine learning workflows. Model Deployment: Facilitate the development, deployment, and scaling of machine learning models in production environments. Design Scalable Data Pipelines: Build and maintain scalable data pipelines and infrastructure to support enterprise-level machine learning deployments. Technical Problem-Solving: Actively analyze and solve technical challenges in machine learning operations.