9 of 9 Permanent Amazon SageMaker Jobs in London

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

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. Master's degree or above in Machine learning/data science, computer science, applied mathematics ...

tidyverse, caret, ggplot, etc.). Proven experience developing, refining, and monitoring NLP models. Familiarity with database management tools and techniques (e.g., SQL, Selenium, S3, Sagemaker, API protocols) is preferred but not required. Understanding model evaluation methods and metrics. Ability to operationalize non-technical ideas into relevant research designs, features ...

Strong Generative AI architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised ...

Strong Generative AI architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised ...

production Strong Python development skills Hands-on experience with LangGraph, LangChain, LlamaIndex, CrewAI or similar Experience with AWS, particularly Bedrock, Lambda, ECS, EKS or SageMaker Strong understanding of RAG, agentic AI and multi-agent architectures Experience with CI/CD, cloud infrastructure and application deployment Comfortable working directly with ...

measure, communicate, and demonstrate the business impact of fraud initiatives. Experience working with cross-functional stakeholders to implement analytical solutions. Exposure to AWS SageMaker and Databricks would be beneficial. How to Apply If you are interested in this Fraud Data Scientist opportunity and would like to learn more, please ...

applications. Agent orchestration, tool calling, RAG, context engineering, vector databases, event-driven integration and human approval workflows. Deployment through Anthropic APIs and/or Amazon Bedrock or Google Cloud Vertex AI, with broader AWS, Google Cloud or Microsoft Azure experience. DevSecOps, containers, infrastructure as code, cloud security, monitoring, scaling … Experience leading engineering delivery, reviewing code, mentoring developers and converting prototypes into maintainable production services. AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices ...

Take AI features from prototype through to secure, reliable production deployment. Design and deploy AI/ML workloads on AWS, using services such as Amazon Bedrock or SageMaker, alongside S3, IAM and Lambda. Assess commercial APIs, hosted models and open-weight alternatives against capability, cost, latency and data … background and a track record of shipping AI/LLM systems into production. Strong hands-on experience deploying AI/ML workloads on AWS. Amazon Bedrock experience would be a significant advantage. Practical experience with RAG, embeddings and processing unstructured data. Confidence designing for security, privacy, tenant isolation ...