Mlops Engineer

About the Client: Join one of the Fortune 500 leaders in the pharmaceutical industry that is looking innovate and expand our technological capabilities. The person would join a product team working on their self-service company-wide platform that enables all the teams and business units to deploy their AI solutions, making them accessible across the entire company.
Position Summary: As an MLOps Engineer, you will play a crucial role in the product team. You will focus on administering and optimizing Databricks within AWS environments, expanding features and capabilities on Databricks, assessing new releases of Databricks features, implementing them to the platform, and generally supporting the business teams with their requests for the platform.
Key Responsibilities: Databricks Administration: Manage and optimize Databricks environments, ensuring high availability, performance, and security. DevOps Engineering: Implement and maintain Databricks on serverless architectures, ensuring seamless CI/CD pipelines and robust integration with AWS services. MLOps Implementation: Develop and enforce best practices for machine learning lifecycle management using Databricks. Collaborate with data scientists and developers to automate and streamline our AI model development. AWS and Azure Integration: Leverage a broad range of AWS services and maintain familiarity with Azure to ensure cross-compatibility and optimal performance of our platforms. Namespace Administration in EKS: Manage Kubernetes namespace-level operations within AWS EKS, including application deployment and environment configuration. Mandatory Requirements: 4+ years in a similar role with proven expertise in Databricks on AWS using services for MLEngineering/MLOps, Infrastructure and Administration Strong background in MLOps, DevOps, and cloud Experience with containerization and orchestration technologies Knowledge of AWS Services and AI Services Optional Requirements:
Experience with using Serverless Databricks or alternatively other serverless services (e.g. Lambdas, StepFunctions) Experience using GenAI-related services on Databricks Exposure to GenAI applications