5 of 5 MLflow Jobs in Wales

distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases). Experience with model explainability techniques (e.g., SHAP, LIME). Familiarity with reinforcement learning or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large … scale systems) Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases #J-18808-Ljbffr More ❯

distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases). Experience with model explainability techniques (e.g., SHAP, LIME). Familiarity with reinforcement learning or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large … scale systems) Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases #J-18808-Ljbffr More ❯

Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps workflows with tools such as MLflow, SageMaker Pipelines. Proven experience building, containerising, and deploying using Docker and Kubernetes. Hands-on experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins) and version control using Git/ More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯