Machine Learning Specialist
2 weeks ago
Requirement:
- Proficiency in Python used both for ML and automation tasks
- Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.
- Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.
- Knowledge of OpenShift / Kubernetes is a must-have.
- Good understanding of ML libraries such as Panda, NumPy, H2O, or TensorFlow.
- Knowledge in the operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g., Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, Dataiku, H2O, or DKube).
- Knowledge of Distributed Data Processing framework, such as Spark, or Dask.
- Knowledge of Workflow Orchestrator, such as Airflow or Ctrl-M.
- Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
- Experience in defining the processes, standards, frameworks, prototypes and toolsets in support of AI and ML development, monitoring, testing and operationalization.
- Experience in ML operationalization and orchestration (MLOps) tools, techniques and platforms. This includes scaling delivery of models, managing and governing ML Models, and managing and scaling AI platforms.
- Knowledge of cloud platforms (e.g. AWS, GCP) would be an advantage.
Soft Skills
- Good knowledge of Devops process and principles
- Strong in Software Engineering fundamentals
- Attention to detail
- Analytical mind and problem-solving aptitude
- Strong Organizational skills
- Visual Thinking
Machine Learning
Utility Industry
Puppet
Airflow
Azure
Pipelines
SageMaker
Shell Scripting
Python
Power Plants
Data Science
Virtual Server
Orchestration
Visual Thinking
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