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 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.
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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.
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