AIML Engineer (Azure Cloud) at DATAMAXIS

*** 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 handson experience with Azure, Docker, and Azure Kubernetes Service (AKS), as well as strong knowledge of cloudnative MLOps best practices.

Responsibilities:

• Design and implement scalable, cloudnative 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 costeffective and reliable deployments.
• Implement robust monitoring, logging, and CICD 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 AIML 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.
  • Handson experience building cloudnative pipelines for model training, scoring, and monitoring.
  • Familiarity with GenAI concepts and tools (experience operationalizing GenAI is a plus).
  • Proficiency in Python, SQL, and Linuxbased development environments.
  • Strong understanding of MLOps principles, CICD pipelines, and productiongrade APIs.
  • Effective communicator with strong problemsolving skills and ability to work across teams.

  • Education

    • Bachelor’s degree in Computer Science, Electronic Engineering, Data Science, or a related field.