MLOps Lead: Sanofi Canada advances healthcare for the whole person and every person. A leading investor in life sciences, manufacturing, and research and development, we create lifechanging and lifesaving products grounded in science that Canadians can trust. Together, we empower self-care, prevent and treat diseases and help people living with illnesses live better.
Why Sanofi? Sanofi has recently embarked on a vast and ambitious digital transformation program. A cornerstone of this roadmap is the acceleration of its data transformation and the adoption of artificial intelligence (AI) and machine learning (ML). We strive to use AI and ML across the organization to accelerate drug discovery, manufacturing, and commercial performance in order to provide life-improving and lifesaving drugs and vaccines to patients faster and more effectively. The Digital Data team at Sanofi is the innovation engine that enables the digital transformation of the company globally. We service our colleagues around the globe to put data to work, creating digital products that provide a competitive advantage for Sanofi. Our data pods enjoy breaking down complex business problems into effective solutions. Combining machine learning knowledge with software and deployment expertise, our MLOps team is dedicated to creating production-grade AI at scale in order to provide valuable insights to our businesses across the globe.
Who You Are: You are a dynamic MLOps Lead interested in challenging the status quo to ensure seamless MLOps that scale up Sanofi's AI solutions for the patients of tomorrow. You are an influencer and leader who has deployed AI/ML solutions with technically robust lifecycle management (e.g., new releases, change management, monitoring, and troubleshooting) and infrastructural support. You have a keen eye for improvement opportunities and a demonstrated ability to deliver using software leading and MLOps skills while working across the full stack and moving fluidly between programming languages and technologies.
Our Tech Stack: We leverage the best-in-class tools and practices to accelerate our analytical builds. For creating production-ready machine learning pipelines and monitoring, we leverage tools such as Python, PySpark, MLFlow, Grafana, and Prometheus. For container technologies, we leverage tools like Docker and Kubernetes. The list of tools continues if we look at GitHub for CI/CD, Terraform, and Ansible for deployment, Argo Workflows for orchestration/scheduling, and much more.
Our vision for digital analytics and AI: Join us on our journey in enabling Sanofi's Digital Transformation through becoming an AI-first organization.
This means:
AI Factory - Versatile Teams Operating in Cross Functional Pods: Utilizing digital and data resources to develop AI products, bringing data management, AI, and product development skills to products, programs, and projects to create an agile, fulfilling, and meaningful work environment.Leading Edge Tech Stack: Experience building products that will be deployed globally on a leading-edge tech stack.World Class Mentorship and Training: Working with renowned leaders and academics in AI and pharma to further develop your skillsets.Do you have what it takes to join our Sanofi Digital Data team?
Job Highlights:
Work in agile pods to design and build cloud-hosted, ML products with automated pipelines that run, monitor, and retrain ML Models.Design and build effective, user-friendly infrastructure to enable scalable, auditable, and maintainable machine learning services.Support lifecycle management of deployed ML apps (e.g., new releases, change management, monitoring, and troubleshooting).Design AI/ML apps and implement automated model and pipeline adaptation and validation working closely with data scientists, engineers, project managers, and more.Walk stakeholders and solution partners through solutions and review product change and development needs.Collaborate with pharma domains to make production-level machine learning code and support creating reusable components in the form of libraries and APIs to accelerate data science build.Research and gain expertise on emerging tools and technologies related to MLOps.An enthusiasm to ask questions and try and learn new things is essential.
Key Functional Requirements & Qualifications:
Graduate degree in Computer Science, Information Systems, Software Engineering, or another quantitative field.5+ years of experience as a MLOps or ML Engineer, preferably building and maintaining machine learning models.Experience developing and maintaining software libraries, following industry standard expectations. Preferably have experience contributing to open-source libraries.Experience in data science, deep learning, statistics, software design, and design thinking.Experience developing CI/CD pipelines for AI/ML development, deploying models to production, monitoring models.
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