Machine Learning Engineer

Technical Skills

Experience as a ML Engineer, working with Python, ML libraries, and large-scale data processing framework.

Advance proficiency in Python used both for ML and automation tasks.

Experience in working with ML libraries such as Panda, NumPy, H2O, or TensorFlow.

Experience in working with large-scale data processing framework, such as Hadoop, Spark, or Dask.

Knowledge of OpenShift / Kubernetes is a must-have.

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 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 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.

Technical Skills

Experience as a ML Engineer, working with Python, ML libraries, and large-scale data processing framework.

Advance proficiency in Python used both for ML and automation tasks.

Experience in working with ML libraries such as Panda, NumPy, H2O, or TensorFlow.

Experience in working with large-scale data processing framework, such as Hadoop, Spark, or Dask.

Knowledge of OpenShift / Kubernetes is a must-have.

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 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 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.