Job FunctionsDesigning and implementing scalable cloud-native ML pipelinesCollaborating with data scientists to operationalize ML models from prototypes to productionManaging deployment of ML models using Azure Machine Learning and AKSDeveloping, containerizing, and orchestrating services using Docker and KubernetesOptimizing cloud data and compute architectures to ensure cost-effective and reliable deploymentsImplementing robust monitoring, logging, and CI/CD practices to support AI operations (MLOps)Working closely with enterprise cloud architects to align AI solutions with customer infrastructure standardsContributing to the evolution of best practices around AI/ML systems in production environments
Job RequirementsMinimum 5 years of experience as a Data Scientist, with at least 2 years focused on machine learning engineering in cloud environmentsProven experience deploying ML models in Azure, preferably with Azure Machine Learning, Docker, and AKSFamiliarity with GenAI concepts and tools (experience operationalizing GenAI is a plus)Proficiency in Python, SQL, and Linux-based development environmentsStrong understanding of MLOps principles, CI/CD pipelines, and production-grade APIs
SkillsHands-on experience with Azure, Docker, and AKSStrong knowledge of cloud-native MLOps best practicesProven experience deploying ML models in Azure, preferably with Azure Machine Learning, Docker, and AKSFamiliarity with GenAI concepts and tools (experience operationalizing GenAI is a plus)Proficiency in Python, SQL, and Linux-based development environmentsStrong understanding of MLOps principles, CI/CD pipelines, and production-grade APIsEffective communicator with strong problem-solving skills and ability to work across teamsBachelor’s degree in Computer Science, Electronic Engineering, Data Science, or a related field
*** 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.
Life at DATAMAXIS
DATAMAXIS takes pride in delivering a wide range of business IT modernization, data analytics, and technology management services. With command of the cutting-edge developments in these fields, our team and consultants are ready to provide you a robust technology modernization experience that results in a big boost in performance capability and operational efficiency.
Thrive Here & What We Value1. Teamwork and collaboration2. Professional growth opportunities3. Hybrid work arrangements4. Career advancement within the company5. Dynamic team environment6. Knowledge sharing culture7. Agile processes8. Mentorship for junior developers9. Global team capabilities10. Customer-focused approach
