1 to 25 of 95 Amazon SageMaker Jobs in the UK

help them achieve business outcomes with AWS. Our projects are often unique, one-of-a-kind endeavors that no one has done before. At Amazon Web Services (AWS), we are helping large enterprises build AI solutions on the AWS Cloud. We apply predictive technology to large volumes of data … our customers. You will leverage the global scale, elasticity, automation, and high-availability features of the AWS platform. You will build customer solutions with Amazon SageMaker, Amazon Bedrock, Amazon Elastic Compute (EC2), Amazon Data Pipeline, Amazon S3, Glue, Amazon DynamoDB, Amazon Relational … Database Service (RDS), Amazon Elastic Map Reduce (EMR), Amazon Kinesis, AWS Lake Formation, and other AWS services. You will collaborate across the whole AWS organization, with other consultants, customer teams, and partners on proof-of-concepts, workshops, and complex implementation projects. You will innovate and experiment to help More ❯

career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's … experience with model customization techniques such as fine-tuning, continued pre-training, and LLM-as-judge evaluation - Experience with optimization of models on GPUs, Amazon Silicon, or TPUs, also experience with open source frameworks for building applications powered by LLMs like LangChain, LlamaIndex, and/or similar tools - Experience … building generative AI applications on AWS using services such as Amazon Bedrock and Amazon SageMaker Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value More ❯

Generative AI Model and Distributed Training, APJ Job ID: Amazon Web Services Korea LLC Do you want to help define the future of Go to Market (GTM) of Machine Learning and Generative AI (GenAI) at AWS? Would you like to join one of the fastest-growing organizations focused on … potential and challenges of GenAI models and applications to engineering teams and C-Level executives. This requires deep familiarity across the stack - compute infrastructure (Amazon EC2, Amazon EKA), ML frameworks PyTorch, JAX, orchestration layers Kubernetes and Slurm, parallel computing (NCCL, MPI), MLOPs, through to Amazon SageMaker Hyperpod, Amazon Bedrock as well as target use cases in the cloud. This is an opportunity to be at the forefront of technological transformations, as a key technical leader. Additionally, you will work with the AWS Generative AI and EC2 product teams to shape product vision and prioritize More ❯

Azure. Experience with business intelligence tools like Tableau or PowerBI. Experience working with LLMs. Experience working with AWS Services like EC2, RDS(Postgres), SQS, Sagemaker, MLflow, S3, API gateway, ECS. Experience in UI frameworks like VueJS is a plus. About Us FactSet creates flexible, open data and software solutions More ❯

Electrical Engineering, Computer Engineering or related field. Experience in containerization - Docker/Kubernetes. Experience in AWS cloud and services (S3, Lambda, Aurora, ECS, EKS, SageMaker, Bedrock, Athena, Secrets Manager, Certificate Manager etc.) Proven DevOps/MLOps experience provisioning and maintaining infrastructure leveraging some of the following: Terraform, Ansible, AWS More ❯

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 ❯

accessing and processing data (PostgreSQL preferred but general SQL knowledge is more important). Familiarity with latest Data Science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g. Tensorflow, MXNet, scikit-learn). Knowledge of software engineering practices (coding practices to DS, unit testing, version control, code review). More ❯

the RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure More ❯

Planning Analytics View more categories View less categories Sector Data Science ,Technology Role Analyst Contract Type Permanent Hours Full Time DESCRIPTION The goal of Amazon Logistics (AMZL) is to build a world class last mile operation. Amazon aims to exceed the expectations of our customers by ensuring that … tools experience: Quicksight/Tableau or similar tools 3. Scripting Experience: R/Python/C++ 4. (Optional) Experience with AWS solutions (S3, Athena, Sagemaker) Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions … priority for Amazon. Please consult our Privacy Notice () to know more about how we collect, use and transfer the personal data of our candidates. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive More ❯

a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs More ❯

a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs 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). What we offer Culture of caring: At GlobalLogic, we prioritize a culture of caring. 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). What We Offer Culture of Caring: At GlobalLogic, we prioritize a culture of caring. More ❯

language models Proven experience with cloud platforms such as AWS, Azure, or Google Cloud Familiarity with tools and frameworks such as TensorFlow, PyTorch, MLflow, SageMaker, or Databricks Deep understanding of data architecture, APIs, and model deployment best practices Knowledge of MLOps and full model lifecycle management Excellent communication and More ❯

techniques, and evaluation strategies . Experience in programming languages such as Python or Java . Understanding of cloud platforms ( Google Cloud , AWS , Vertex AI , Sagemaker ). Bonus points: Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Experience with data analysis or data science. The experience, skills, and More ❯

and unsupervised learning techniques Experience in demand prediction, optimisation, or computer vision is advantageous Comfortable working with cloud platforms (preferably AWS) and services like SageMaker or Lambda Strong mathematical and statistical foundations, with a sharp eye for patterns and insights Willingness to build basic backend development skills (Python/ More ❯

Collaborate with ML/AI Teams Package and deploy large‑language‑model (LLM) training jobs on distributed GPU clusters (Slurm, Ray, Kubeflow, or AWS SageMaker). Optimize model‑serving (Triton, vLLM, TorchServe) for low‑latency, high‑throughput inference. Cost & Performance Optimization Track cloud spend, right‑size resources, and introduce More ❯

be fluent in Python - writing readable, testable, and maintainable code comes naturally to you. You'll be comfortable with cloud-based tools like AWS SageMaker and Lambda, and you'll know your way around Git, SQL, and common Python data science libraries (like pandas/polars, scikit-learn, xgboost More ❯

down complex challenges, devise practical solutions, and iterate quickly based on feedback or data. Hands-on experience with cloud platforms, including AWS (e.g., EC2, Sagemaker, Bedrock, S3, Lambda), Google Cloud (e.g., Compute Engine, Vertex, GKE, BigQuery), Azure (e.g., Virtual Machines, Azure OpenAI) etc. Proficiency in scripting or programming (e.g. 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 ❯

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 ❯

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 ❯