1 to 25 of 27 Amazon SageMaker Jobs in the UK excluding London

3+ years of experience in machine learning operations, data engineering, or related roles AWS Proficiency: Strong understanding of AWS services (e.g., EC2, S3, Lambda, SageMaker, ECS) and cloud infrastructure management Programming and ML Frameworks: Proficiency in Python and experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch More ❯

machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. Bachelor's degree (master's or higher strongly preferred) in machine learning, computer science, data science, applied More ❯

and deploying cloud-based data solutions on AWS, Azure, or Google Cloud. Proficiency in AI/ML ecosystems such as Azure ML, Databricks, MLflow, Amazon Sagemaker and/or Bedrock with experience deploying and monitoring models in production. Understanding of data governance, security, and compliance frameworks, including GDPR More ❯

driven products or solutions within commercial or public sector environments. Knowledge, Skills, and Experience (Desirable): Experience with cloud platforms (Azure ML, Azure Databricks, AWS SageMaker). Familiarity with Defence, public sector, healthcare, or similar industry verticals. Advanced qualifications (Masters or PhD) in AI, machine learning, computer science, or a More ❯

such as Power BI and OAS. Understanding of data replication tools such as Oracle GoldenGate and AWS DMS. Knowledge of AWS Services such as SageMaker, Lambda, Step Functions, S3 etc would be an advantage. Hands on and technical understanding of complex Data Warehousing terminology. Python scripting knowledge would be More ❯

business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages More ❯

business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages More ❯

experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. More ❯

experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. More ❯

experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. More ❯

to automate and manage data systems so they run smoothly and can grow easily. You have experience with tools like AWS (S3, Glue, Redshift, SageMaker) or other cloud platforms. Youre also familiar with Docker, Terraform, GitHub Actions, and Vault for managing secrets. You can code in SQL, Python, Spark More ❯

demonstrate how to automate and manage data systems so they run smoothly and can grow easily. Experience with tools like AWS (S3, Glue, Redshift, SageMaker) or other cloud platforms. Familiarity with Docker, Terraform, GitHub Actions, and Vault for managing secrets. Proficiency in SQL, Python, Spark, or Scala to work More ❯

grow a diverse team who are geographically dispersed. Comfortable using a multi-platform stack to deliver products (Databricks, Azure ML Studio, Azure and Snowflake, SageMaker, AWS). Ability to take ownership of a broad data product-set and drive forward the delivery of new products and product features. Strong More ❯

Proficiency in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI More ❯

Proficiency in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI More ❯

working with large data sets with a mathematical and analytical mindset. They are looking for senior engineers to join the team working with AWS SageMaker for AI and ML as well as research and cleansing of data sets. #J-18808-Ljbffr More ❯

models from OAS using oapi-codegen Launch Darkly Feature Flagging Docker AWS Cloud Services including EKS and RDS AWS Bedrock Knowledgebases and Agents AWS Sagemaker Generative AI Prompt Engineering Additional Information About QAD: QAD Inc. is a leading provider of adaptive, cloud-based enterprise software and services for global More ❯

in solution design, data engineering, and AI/ML platform construction. Good with artificial and machine learning systems (Google Vertex AI, Azure ML, AWS SageMaker) and contemporary data tools (BigQuery, Databricks, Snowflake, Spark). Good grasp of accountable artificial intelligence, ethics, and model explainability. Proficient in integrating artificial intelligence More ❯

Backend Engineers and DevOps teams to transform machine learning research into scalable, production-ready applications. The ideal candidate is experienced in MLOps, AWS Bedrock, SageMaker, data pipelines, and model lifecycle management. You must be able to support the full lifecycle from taking code in notebook into production and know … to-end ML lifecycle, from development to deployment and maintenance. Implement ML model versioning, reproducibility, documentation and governance best practices. Work with AWS Bedrock, SageMaker, Lambda, Fargate, and ECS to deploy ML models at scale. Develop feature stores, data pipelines, and real-time inference architectures. Improve model explainability, bias … 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar. Experience deploying and managing ML models in production using AWS (Bedrock, SageMaker, Lambda, S3, SQS, DynamoDB, etc.). Familiarity with NLP, LLMs, or AI-powered SaaS applications, such as the Claude Foundation Model from Anthropic. Strong More ❯

in data analysis and machine learning. Some form of experience in demand/forecast modelling Strong experience with AWS or similar (ideally experience with SageMaker) Familiarity with scenario-based modelling On offer in return is an exciting role that will develop and make a real impact, with a forward More ❯

the perfect role for you! Key Skills: Strong experience with Python, Rust, or Go. Experience in the use of AI tools within AWS (eg. Sagemaker or Bedrock) OR Azure (eg. Azure ML or Azure AI Studio) Excellent knowledge of .NET/C#, MS SQL Server and other Microsofttechnologies Desirable More ❯

delivery experience and research We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB, AI/ML architectural patterns and best practices e.g. data More ❯

understanding technology choices available and mapping the most appropriate data technology to each use case Expertise in designing, modelling and optimising using relational (e.g. Amazon, RDS), NoSQL (e.g. DynamoDB, DocumentDB) and data warehouses (e.g. Redshift) Expertise in designing scalable, high performance data platform architecture using AWS native or marketplace More ❯

and deploy ML Recommendation systems. Below are some of the tools/technologies we currently use in our teams: • ML/AI: MLOps, AWS SageMaker • Languages: Python, TypeScript, JavaScript • Cloud: AWS (S3, EC2, SQS, Lambda, ElasticSearch/OpenSearch, etc) • Deployment: Codebuild, CodePipeline. • Testing: TDD, BDD, end-to-end, load … Stream-Processing: Apache Airflow, Beam, Kafka, Flink • Techniques: MLOps, infrastructure, CI/CD. We create and support reliable and scalable cloud-based services on Amazon Web Services, use Git and GitHub for version control and practice pair programming and Test-Driven Development where possible. In a nutshell, we are More ❯

is essential, with preferred expertise in demand prediction, computer vision, or optimisation. Familiarity with the AWS cloud platform, particularly its AI/ML services (SageMaker and Lambda) and related data processing tools, is also required. This is a fantastic opportunity for a Senior Data Scientist to join a company … Scientist with knowledge of Python AI libraries. Expertise in demand prediction, computer vision, or optimisation (preferred). Familiarity with AWS AI/ML services (SageMaker, Lambda). Reference Number: BBBH251711 More ❯