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Follow →Build and maintain ML pipelines on Azure Databricks and MLFlow, orchestrating data workflows with Azure Data Factory for production-grade AI systems.
Build and maintain scalable ML pipelines on Azure, automating training, deployment, and monitoring with DevOps practices and Kubernetes.
Builds and maintains automated ML pipelines, model serving infrastructure, and scalable inference systems for an AI company using PyTorch, TensorFlow, Kubernetes, and cloud platforms.
Design and maintain the MLOps platform that lets data scientists train, deploy, and monitor AI models at scale in AWS, using tools like MLflow and CI/CD pipelines.
Build and maintain ML pipelines, deploy models via APIs/containers, and monitor performance for government AI projects using cloud-native tools.
Build and maintain the infrastructure and pipelines to deploy AI/ML models from experimentation to production, ensuring scalability, monitoring, and reliability in a cloud environment.