***** 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.