*** Remote work is optional for top candidates *** As an AI Engineer on the Data Science team, you will play a key role in productionizing machine learning models, building robust pipelines, and enhancing the overall AI platform. This role requires hands-on experience with Azure, Docker, and Azure Kubernetes Service (AKS), as well as strong knowledge of cloud-native MLOps best practices. Responsibilities: + Design and implement scalable, cloud-native ML pipelines for production AI solutions. + Collaborate with data scientists to operationalize ML models from prototypes to production. + Manage deployment of ML models using Azure Machine Learning and AKS. + Develop, containerize, and orchestrate services using Docker and Kubernetes. + Optimize cloud data and compute architectures to ensure cost-effective and reliable deployments. + Implement robust monitoring, logging, and CI/CD practices to support AI operations (MLOps). + Work closely with enterprise cloud architects to align AI solutions with customer infrastructure standards. + Contribute to the evolution of the best practices around AI/ML systems in production environments. Qualifications: + Minimum 5 years of experience as a Data Scientist, with at least 2 years focused on machine learning engineering in cloud environments. + Proven experience deploying ML models in Azure, preferably with Azure Machine Learning, Docker, and AKS. + Hands-on experience building cloud-native pipelines for model training, scoring, and monitoring. + Familiarity with GenAI concepts and tools (experience operationalizing GenAI is a plus). + Proficiency in Python, SQL, and Linux-based development environments. + Strong understanding of MLOps principles, CI/CD pipelines, and production-grade APIs. + Effective communicator with strong problem-solving skills and ability to work across teams. Education + Bachelor's degree in Computer Science, Electronic Engineering, Data Science, or a related field.