MLOps Engineer

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

Mandatory Skills :

Good to Have Skills :