Mlops Engineer

Job Description

Key Responsibilities Responsible for building and maintaining robust machine learning pipelines ensuring efficient model deployment monitoring and lifecycle management within a cloud-based environment Extensive expertise in MLOps specifically with Google Cloud Platform GCP and Vertex AI and a deep understanding of model performance drift detection and GPU accelerators Build and maintain scalable MLOps pipelines in GCP Vertex AI for endtoend machine learning workflows Manage the full MLOps lifecycle from data preprocessing model training and deployment to model monitoring and drift detection Implement realtime model monitoring and drift detection to ensure optimal model performance over time Optimize model training and inference processes using GPU accelerators and CUDA Collaborate with cross functional teams to automate and streamline machine learning model deployment and monitoring Utilize Python 310 with libraries such as pandas NumPy and TensorFlow to handle data processing and model development Set up infrastructure for continuous training testing and deployment of machine learning models Ensure scalability security and high availability in all machine learning operations by implementing best practices in MLOps Requirements 5 years of experience in MLOps and building ML pipelines 3 years of experience in GCP Vertex AI Deep understanding of the MLOps lifecycle and automation of ML workflows Proficient in Python 310 and related libraries such as pandas NumPy and TensorFlow Strong experience in GPU accelerators and CUDA for model training and optimization Proven experience in model monitoring drift detection and maintaining model accuracy over time Strong problemsolving skills with the ability to work in a fast paced environment Knowledge of data versioning and model version control techniques Familiarity with TensorFlow Extended TFX or other ML workflow orchestration frameworks