2 of 2 Amazon SageMaker Jobs in Salford

and maintaining tools that support data science and MLOps/LLMOps workflows. Collaborate with Data Scientists to deploy, serve, and monitor LLMs in real-time and batch environments using Amazon SageMaker, Bedrock Implement Infrastructure-as-Code with AWS CDK, CloudFormation to provision and manage cloud environments. Build and maintain CI/CD pipelines using GitHub Actions, AWS CodePipeline …/MLOps experience with a strong focus on building and delivering scalable infrastructure for ML and AI applications using Python and cloud native technologies Experience with cloud services, especially Amazon Web Services (AWS) – SageMaker, 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/GitHub. Strong understanding of DevOps concepts including blue/green deployments, canary releases, rollback strategies, and infrastructure automation. Familiarity with security and compliance More ❯

Data Science, MLOps, and LLMOps workflows across the organisation. Drive strategy and execution for deploying, serving, and monitoring large language models (LLMs) in real-time and batch environments using Amazon SageMaker, Bedrock, and related services. Guide the use of Infrastructure-as-Code (IaC) practices with AWS CDK and CloudFormation to provision and manage secure and maintainable cloud environments. … experience in DevOps/MLOps roles with demonstrated impact in building, scaling, and securing ML/AI infrastructure in cloud-native environments. Strong experience with AWS services such as SageMaker, Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS, with a strong command of cloud solution architecture. Advanced proficiency 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 with hands-on expertise in GitHub Actions, Jenkins, and More ❯