The ideal candidate for this role should possess a strong blend of MLOps and ML engineering skills.Technological Stack: Python, AWS services (ECS, Batch Compute, S3, Timestream, IAM, CloudWatch, Athena), Airflow for orchestration, MLflow for model versioning, GitLab CI, and eventually, AWS SageMaker pipelines.Data Engineering Skills: Expertise in pipeline monitoring, including getting data from Bulk CDM, investigating query issues, and sending output data to ALTO.ML Engineering Skills: Experience with debugging forecast and clustering models, setting up and automating CI/CD for ML workflows, developing and deploying end-to-end ML pipelines (data preparation, training, validation, deployment), and a strong background in ML frameworks like LightGBM.MLOps Engineer to handle the tickets, and Data Engineering tickets/development
