MLOps Engineer with Python and TensorFlow

- Operationalize machine learning models by designing, building, and maintaining end-to-end MLOps pipelines that facilitate model training, validation, deployment, and automatic retraining across various AI applications. - Deploy batch and real-time inference workloads utilizing cloud-native services and containerized setups, ensuring optimal performance, reliability, and cost efficiency. - Implement comprehensive monitoring systems for model performance, data drift, prediction quality, latency, and overall system health, establishing alerting protocols for quick issue identification and resolution. - Develop and maintain CI/CD workflows for machine learning assets, encompassing code, features, models, and configurations to enable secure and consistent releases. - Collaborate with data engineering teams to guarantee reliable data ingestion, feature generation, and versioning, ensuring consistent model behavior across different environments, including the design, construction, and support of online and offline feature stores. - Assist in enterprise AI governance by facilitating model lineage, reproducibility, audit trails, and controlled promotions in line with Responsible AI principles. - Collaborate with data scientists, AI engineers, product managers, IT, and cybersecurity teams to produce and implement modeling work as production-ready services. - Contribute to shared MLOps tools, standards, and reference architectures that promote swift AI delivery across Weyerhaeusers AI Factory. - Identify and capitalize on opportunities to enhance reliability, automation, scalability, and developer experience throughout the AI delivery lifecycle.