Risk Platform Developer

We are building the foundational technology layer for Novartis' next-generation AI/ML-driven Advanced Analytics capability. As a Platform Engineer, you will play a pivotal role in setting up the technical scaffolding that enables data scientists and AI engineers to scale their efforts rapidly and securely. This role requires hands-on engineering depth, a strategic mindset, and a passion for enabling AI-driven transformation in the healthcare domain. Usted podría ser el solicitante perfecto para este trabajo. Lea toda la información asociada y asegúrese de presentar su candidatura. Key Responsibilities Design and build secure, scalable, and resilient data & ML platforms to enable rapid AI experimentation and deployment. Set up and optimize infrastructure on AWS (SageMaker, EKS, Lambda, Glue, Step Functions, Fargate) and Azure DevOps pipelines. Implement CI/CD workflows, monitoring, and logging for MLOps. Own the end-to-end environment lifecycle including provisioning, destruction, and rebuild of infrastructure and pipelines. Design for distributed computing, dynamic late binding, and dependency injection across hybrid cloud environments. Collaborate with data engineers and scientists to deploy and optimize high-performance data pipelines using modern orchestration tools. xiphteb Partner with AI engineers, DevSecOps, data scientists, and delivery managers to ensure fast onboarding and alignment with business objectives. Technical Skills & Requirements Deep experience in AWS services (EKS, SageMaker, Lambda, Glue, Step Functions, Fargate) Working knowledge of Azure, particularly Azure DevOps and integration tooling Proficient in Terraform, Helm, Docker, Kubernetes Experience with CI/CD using GitHub Actions, Jenkins, or Azure DevOps Strong in distributed computing architecture Hands-on understanding of dependency injection and late-binding systems Strong scripting (Python, Bash) and automation mindset Background in healthcare, life sciences, or related regulated industries Experience building ML/AI platforms for model deployment, experimentation tracking, and governance Exposure to hybrid or multi-cloud environments J-18808-Ljbffr