ML Engineer in Sri Lanka at Dialog

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ML Engineer

Dialog

| 2024-05-27

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Study and transform data science prototypes into deployable prototypes by defining decision boundaries
Define the pre-processing or feature engineering to be done on a given dataset
Train models, tune their hyperparameters and define validation strategies
Define the inferencing and maintain connectivity with serving infrastructure.
Utilize and leverage high-end MLOps tools and technologies(E.g.: Sagemaker).
Design and develop containerized model artifacts using Docker or Kubenetes.
Design and orchestrate ML pipelines for automation using Cloud tool stacks (E.g.: Sagemaker pipelines, Code pipelines)
Monitor and troubleshoot of in-production DS models and tools by analysing the errors of the model and designing strategies to overcome them
Build and maintain production-level python libraries.
Execute best practices in version control and continuous integration / delivery through Code repository (E.g.: Code commit, GitLab)
Alliance with Data Scientists, Engineers and other key stakeholders
Research and implement MLOps tools, frameworks and platforms for Data Science projects.
Proactively introduce a modern, Agile and Automated approach to Data Science.