"Alerted.org

* 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.