As an ML Engineer, your pivotal role involves operationalizing ML Models developed by Client data scientists.
Technical Skills
- 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.
- ML model refactoring, optimization, containerization, deployment, and quality monitoring
- 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.
