26 to 50 of 224 Amazon SageMaker Jobs in London

within large organisations, through e.g. 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, Google Cloud) including Cloud provisioning More ❯

tools (e.g., Docker, Kubernetes). 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. Across every region and department More ❯

PyTorch, or similar. Experience with data science tools and platforms (e.g., Jupyter, Pandas, NumPy, MLFlow). Familiarity with cloud-based AI tools and infrastructure, especially within the AWS ecosystem (SageMaker, Lambda, etc.). Strong understanding of data structures, algorithms, and statistical analysis. Experience working with ETL pipelines and structured/unstructured data. Solid grasp of software engineering principles, version More ❯

lead technical position, ideally within 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, model fine-tuning, and building More ❯

lead technical position, ideally within 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, model fine-tuning, and building More ❯

product environments or a data-rich business. Tech-savvy : Proficient in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX). Strong command of cloud- AI tooling (e.g., AWS SageMaker, GCP Vertex AI, AzureML/AzureAI, MLflow, Weights & Biases). Well-rounded Engineer : Comfortable working with version-controlled codebases, DevOps pipelines, containerisation/microservices (Docker/Kubernetes), and Infrastructure More ❯

tools (e.g., Docker, Kubernetes). 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. Across every region and department More ❯

Exposure/experience in containerization technologies like docker, Kubernetes, AWS EKS etc. Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g. AWS Bedrock, AWS S3, AWS Sagemaker, Azure AI search, Azure OpenAI, Azure blob storage etc. What Would Make You Stand Out: Passionate about the power of data, GenAI and predictive analytics to drive better business More ❯

tools (e.g., Docker, Kubernetes). • 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. Across every region and department More ❯

within large organisations, through e.g. 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, Google Cloud) including Cloud provisioning More ❯

within large organisations, through e.g. 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, Google Cloud) including Cloud provisioning More ❯

scale data workflow orchestration platforms (e.g., Airflow). Leverage expertise in cloud computing platforms (AWS and Azure) to build and optimize AI infrastructure, using services like AWS Bedrock, S3, SageMaker, Azure AI Search, etc. Champion ML governance by ensuring guidelines are followed, monitoring SLAs, and continuously improving the performance and reliability of AI solutions. Translate complex data science and More ❯

Job Title Senior Machine Learning Engineer Job Description Here at UnderwriteMe, we are on a mission to make life insurance more widely accessible and ensure people and their loved ones are protected when the inevitable happens. We are doing this More ❯

Senior Machine Learning Engineer Apply locations London, UK time type Full time posted on Posted Today job requisition id R13623 Job Title : Senior Machine Learning Engineer Job Description Here at UnderwriteMe, we are on a mission to make life insurance More ❯

/or AWS Machine Learning Specialty Proven track record of architecting, deploying, and optimizing complex cloud solutions for AI-driven workloads. Generative AI and Cutting-Edge Technologies: Expertise in Amazon Bedrock for deploying foundation models and managing scalable GenAI workloads. Proficiency in fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker JumpStart … and Hugging Face on AWS. Experience leveraging Amazon Q (formerly AWS CodeWhisperer) Business and Developer for AI-powered coding productivity and automation. Machine Learning Frameworks and Pipelines: In-depth knowledge of Amazon SageMaker, including Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability. Skilled in distributed model training with multi-GPU clusters … and optimization for high-performance inference. Data Engineering and AI Workflow Optimization: Proficient in building data pipelines with Amazon S3, AWS Glue, Lake Formation, and Redshift for AI and ML workloads. Experienced in optimizing data preparation for large-scale AI model training and inference workflows. AI Integration and Deployment: Expertise in building end-to-end AI pipelines using AWS More ❯

Delivery Practice Manager - Data Analytics, ASEAN Professional Services The Amazon Web Services Professional Services (ProServe) team is seeking an experienced Delivery Practice Manager (DPM) to join our ProServe Shared Delivery Team (SDT) at Amazon Web Services (AWS). In this role, you'll manage a team of ProServe Delivery Consultants while supporting AWS enterprise customers through transformative projects. … to address customer outcomes. Possessing the ability to translate technical concepts into business value for customers and then talk in technical depth with teams, you will cultivate strong customer, Amazon Global Sales (AGS), and ProServe team relationships which enables exceptional business performance. DPMs success is primarily measured by consistently delivering customer engagements by supporting sales through scoping technical requirements … candidates to apply. If your 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 why customers from the most More ❯

role could be extended to a longer DevOps contract. What You'll Do - Design and build an end-to-end MLOps pipeline using AWS , with a strong focus on SageMaker for training, deployment, and hosting. - Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility. - Architect and implement a feature store strategy for consistent, discoverable, and reusable features … across training and inference environments (e.g., using SageMaker Feature Store , Feast, or custom implementation). - Work closely with data scientists to formalize feature engineering workflows , ensuring traceability, scalability, and maintainability of features. - Develop unit, integration, and data validation tests for models and features to ensure stability and quality. - Establish model monitoring and alerting frameworks for real-time and batch … data teams to adopt new MLOps practices. What We're Looking For - 3+ years of experience in MLOps, DevOps, or ML infrastructure roles. - Deep familiarity with AWS services , especially SageMaker , S3, Lambda, CloudWatch, IAM, and optionally Glue or Athena. - Strong experience with MLflow , experiment tracking , and model versioning. - Proven experience setting up and managing a feature store , and driving More ❯

including deep learning techniques, knowledge distillation, prompt engineering, and fine-tuning. 5-8 years of industry experience with a minimum of 2 years of experience working in AWS & AWS Sagemaker and developing Sagemaker pipelines. Knowledge of Jenkins, CloudFormation, Terraform code & MLOps practices. Exhibit proficiency in Python, software engineering practices, along with expertise in deep learning frameworks (ML/ More ❯

non-technical stakeholders, effectively bridging the gap between technical and non-technical teams. Good command of analytical programming and visualization libraries (e.g., R, Matplotlib, ggplot) and supporting tools (e.g., Sagemaker, VS Code). Job Benefits 25 days paid holiday in addition to bank holidays to relax and refresh throughout the year. Hybrid Working: We work in the office More ❯

CD and API design Hands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment, as well as ML platforms (, AWS Sagemaker, Azure Machine Learning studio) Be a ‘lifelong learner’ and can demonstrate a drive to always be learning and developing your skillsets and develop the skillsets of others around you More ❯

clusters. Good knowledge of DevOps tools and technologies, such as Helm, Docker, Terraform and CI/CD pipelines (GitHub Actions). Knowledge of MLOps especially on cloud environments: Vertex, Sagemaker, Synapse, is a huge plus. Strong Knowledge of the software development lifecycle (code review, version control, tooling, testing, etc.). Understanding of the full stack would be ideal (REST More ❯

classification, decision trees, support vector 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 mathematics or related technical field More ❯

access. Experience building underlying data pipelines and ETL, particularly useful if done using AWS or tools such as dbt. Experience working in an AWS Data Stack (AWS Glue, S3, Sagemaker, BedRock etc.). Application Requirements When applying for a position with Quotient Sciences to be able to work in our organisation you must be aged 18 years or over More ❯

a trajectory to success? Are you familiar with security best practices and compliance standards for AI applications? Do you want to be part of the team helping to establish Amazon Web Services as a leading technology AI platform? Would you like to be part of a team that genuinely focuses on understanding what's best for the customer and … also be valued within as a technical expert? Amazon Web Services is looking for a skilled and motivated Professional Services Senior Data Scientist to help accelerate our growing Data and AI business in the UK and work with our public sector customers. We need passionate, experienced consultants to help our citizens and the community benefit from the AI revolution. … ambiguity and mentoring junior teams. Expertise: Collaborate with field sales, pre-sales, training and support teams to help partners and customers learn and use AWS services such as Bedrock, Sagemaker, and other data services. Experience in architecture, engineering, software design and operations in hybrid environments as well as complex projects at scale. Solutions: Demonstrated consulting skills, ideally through previous More ❯

a trajectory to success? Are you familiar with security best practices and compliance standards for AI applications? Do you want to be part of the team helping to establish Amazon Web Services as a leading technology AI platform? Would you like to be part of a team that genuinely focuses on understanding what's best for the customer and … also be valued within as a technical expert? Amazon Web Services is looking for a skilled and motivated Professional Services Data Scientist to help accelerate our growing Data and AI business in the UK and work with our public sector customers. We need passionate, experienced consultants to help our citizens and the community benefit from the AI revolution. Candidates … customers deconstruct ambiguity. Responsibilities include: Expertise: Collaborate with field sales, pre-sales, training and support teams to help partners and customers learn and use AWS services such as Bedrock, Sagemaker, and other data services. Experience in architecture, software design and operations in hybrid environments as well as complex projects at scale. Solutions: Demonstrated consulting skills, ideally through previous roles More ❯