Job
Description
Design, implement, and maintain data pipelines for processing large datasets, ensuring data availability, quality, and efficiency for machine learning model training and inference. Collaborate with data scientists to streamline the deployment of machine learning models, ensuring scalability, performance, and reliability in production environments. Develop and optimize ETL (Extract, Transform, Load) processes, ensuring data flow from various sources into structured data storage systems. Automate ML workflows using ML Ops tools and frameworks (e. g. , Kubeflow, MLflow, TensorFlow Extended (TFX)). Ensure effective model monitoring, versioning, and logging to track performance and metrics in a production setting. Collaborate with cross-functional teams to improve data architectures and facilitate the continuous integration and deployment of ML models. Work on data storage solutions, including databases, data lakes, and cloud-based storage systems (e. g. , AWS, GCP, Azure). Ensure data security, integrity, and compliance with data governance policies. Perform troubleshooting and root cause analysis on production-level machine learning systems. Skills: Glue, Pyspark, AWS Services, Strong in SQL; Nice to have : Redshift, Knowledge of SAS Dataset Mandatory Competencies DevOps - CLOUD AWS DevOps - Docker ETL - AWS Glue DevOps - Kubernetes Database - SQL Big Data - PySpark Database - Redshift Cloud - Azure Data Science - Azure ML Data on Cloud - Azure Data Lake (ADL) Beh - Communication and collaboration At Iris Software, we offer world-class benefits designed to support the financial, health and well-being needs of our associates to help achieve harmony between their professional and personal growth. From comprehensive health insurance and competitive salaries to flexible work arrangements and ongoing learning opportunities, were committed to providing a supportive and rewarding work environment. Join us and experience the difference of working at a company that values its employees success and happiness.
