* 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.