DescriptionWe are looking for an ML Engineer to develop and scale our AI platform build robust ML pipelines and enable the deployment of AI workloads.
What You Will Be Working On
- Design build and maintain scalable ML infrastructure using tools like Kubeflow MLflow and RunAI.
- Automate the setup and management of ML environments and pipelines to ensure consistency and reliability.
- Ensure automated testing and validation of AI/ML models before deployment.
- Monitor model performance and health using tools for logging monitoring and alerting.
- Implement strategies for model retraining and updating based on performance metrics.
- Collaborate with data scientists engineers and stakeholders to translate requirements into scalable ML solutions.
- Participate actively in agile ceremonies such as sprint planning daily stand-ups retrospectives and backlog grooming.
RequirementsSkilled in Linux and Python with knowledge of JavaScript or other scripting languages.
Experience with containerized applications (Docker Kubernetes) and ML infrastructure tools such as Kubeflow MLflow and RunAI.
Familiarity with end-to-end ML lifecycle management including model development training validation and deployment.
Strong problem-solving skills and ability to troubleshoot complex issues.
Excellent communication and collaboration skills.
Ability to work in a fast-paced dynamic environment.
Continuous learning mindset to keep up with technological advancements.
Preferably minimum 2 years of relevant working experience.
DescriptionWe are looking for an ML Engineer to develop and scale our AI platform build robust ML pipelines and enable the deployment of AI workloads. What You Will Be Working On Design build and maintain scalable ML infrastructure using tools like Kubeflow MLflow and RunAI. Automate the setup an...
DescriptionWe are looking for an ML Engineer to develop and scale our AI platform build robust ML pipelines and enable the deployment of AI workloads.
What You Will Be Working On
- Design build and maintain scalable ML infrastructure using tools like Kubeflow MLflow and RunAI.
- Automate the setup and management of ML environments and pipelines to ensure consistency and reliability.
- Ensure automated testing and validation of AI/ML models before deployment.
- Monitor model performance and health using tools for logging monitoring and alerting.
- Implement strategies for model retraining and updating based on performance metrics.
- Collaborate with data scientists engineers and stakeholders to translate requirements into scalable ML solutions.
- Participate actively in agile ceremonies such as sprint planning daily stand-ups retrospectives and backlog grooming.
RequirementsSkilled in Linux and Python with knowledge of JavaScript or other scripting languages.
Experience with containerized applications (Docker Kubernetes) and ML infrastructure tools such as Kubeflow MLflow and RunAI.
Familiarity with end-to-end ML lifecycle management including model development training validation and deployment.
Strong problem-solving skills and ability to troubleshoot complex issues.
Excellent communication and collaboration skills.
Ability to work in a fast-paced dynamic environment.
Continuous learning mindset to keep up with technological advancements.
Preferably minimum 2 years of relevant working experience.
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