Assist in design, development, and deployment of automated machine learning workflows to support model training, evaluation, and deployment; Assist with the building of workflows and automations in the Rewst platform; Build and maintain data pipelines for ingestion, preprocessing, and feature engineering to support continuous learning systems; Collaborate with team members to optimize model performance and automate retraining processes; Implement and support monitoring and logging processes to track system performance and reliability; Support automated testing, validation, and deployment processes to ensure reliability and scalability of AI solutions; Maintain version control and reproducibility of models, datasets, and code using Git and MLOps best practices; Troubleshoot automation workflows and assist with root cause analysis and system improvements; Ensure adherence to data governance, security, privacy, and ethical AI standards; Document workflows, configurations, and technical processes for maintainability and audits; Participate in internal and client-related development projects as assigned. JOB REQUIREMENTS: Bachelors degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical field; 12 months experience in IT support or related technical role (F/T, P/T or aggregate) Candidate must pass drug and background test before hire. Proficiency in Python, or Java or, or R Proficiency in scripting tools, i.e. Jinja Familiarity with front in Typescript frameworks, i.e. React Experience with machine learning libraries, i.e. TensorFlow, PyTorch, or Scikit-learn Familiarity with workflow orchestration and automation tools, i.e. , Airflow, Kubeflow, Jenkins Familiarity with automation platforms, i.e. Rewst Automation or similar platform Basic understanding of cloud platforms, i.e. AWS, Azure, or Google Cloud Knowledge of data pipelines, ETL processes, APIs, and data automation concepts Experience with containers and deployment tools, i.e. Docker Familiarity with Git and CI/CD pipelines.
Not specified in the original listing.
Not specified in the original listing.
