4 of 4 Amazon SageMaker Jobs in the North of England

solutions in complex enterprise environments Experience in all architecture domains:- application, data, cloud ( AWS) and technology with expertise with AI, Gen Ai and ML frameworks (e.g., TensorFlow, PyTorch, AWS Sagemaker, AWS Bedrock, OpenAI). Strong experience of AI services and AI lifecycle and model training Proficiency in backend and frontend technologies such as .NET Core, Node.js, and React; familiarity More ❯

Have technical expertise in one or more of the following technology areas: NoSQL, such as DocumentDB/MongoDB RDF Graph database such as GraphDB ML/AI such as Sagemaker/Bedrock Search technologies such as SOLR or Opensearch/ElasticSearch Data pipeline engineering utilising cloud-based technologies (AWS) Write high quality clean, testable code, with a focus on More ❯

modern frameworks. Build and optimize RAG pipelines using models like GPT, Claude, or other LLMs integrated with document retrieval systems. Develop production-ready ML applications in cloud environments (AWS, SageMaker, Databricks, etc.). Leverage GPU-based computing resources and CUDA/Nvidia for performance optimization in training and inference. Collaborate with data engineers, software developers, and product teams to … experience with PyTorch, TensorFlow, Scikit-learn, and transformer-based models. Practical knowledge of LLM integration (e.g., GPT, Claude) and RAG architecture. Experience with ML lifecycle management tools like AWS SageMaker, MLflow, or Databricks. Working knowledge of CUDA, Nvidia GPUs, and distributed training. Experience with AWS services (S3, Lambda, EC2, SageMaker, Bedrock, etc.). Desired: Experience deploying models as More ❯

mentored by a friendly development team, and the BBC will provide many opportunities for learning as you progress. We create and support reliable and scalable cloud-based services on Amazon Web Services, use GitHub for version control and practice pair programming and Test-Driven Development where possible. Hands-on experience with Python development and an understanding of software engineering … best practices (e.g., TDD, clean code principles). Experience of AWS services such as SageMaker, S3, EC2, VPC, Lambda, Redshift Experience of DevOps practices, including containerisation with Docker and CI/CD pipelines. Exposure to MLOps tools (e.g., MLFlow) and practices, with a willingness to learn more. Exposure to infrastructure-as-code tools (CDK, CloudFormation) Knowledge of feature engineering More ❯