Overview
On Site
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 4 Month(s)
Skills
Cloud
Azure
DOCKER
AKS
MLOps
ML
AL
Job Details
Job Summary: 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).
- 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.
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