21 of 21 Amazon SageMaker Jobs in the Midlands

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

large language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

management. Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery). Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques ...

working arrangement is in place. About you To be successful in this role its essential you have: Extensive experience with AWS (S3, Glue, Redshift, SageMaker) Experience of building and automating data pipelines in finance The ability to demonstrate, automate and manage data systems so they run smoothly ...

scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical concepts clearly to both technical and non-technical stakeholders using appropriate language and methods. Profiling data and analysing ...

large-scale analytics engines (e.g. Spark/PySpark) and scripting languages such as Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage formats or frameworks (e.g. Parquet, Delta Lake, Fabric). Experience with DevOps/DataOps tooling and practices ...