1 to 25 of 66 Amazon SageMaker Jobs in the UK

Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within: Microsoft Azure Google Cloud Platform (GCP) Additional experience with: Docker Kubernetes Git CI/CD Pipelines ...

operate, support, and secure mission-critical applications\ NPreferred qualifications, capabilities and skills\ N\n\nExperience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow\ N\n\nExposureto cloud-native microservices architecture\ N\n\nFamiliarity with advanced AI/ML concepts ...

Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred qualifications, capabilities and skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols ...

Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred qualifications, capabilities and skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols ...

distributed teams Continuous learning mindset with a focus on emerging technologies and innovation Nice-to-Have Experience with AWS or GCP ML platforms (e.g., SageMaker, Vertex AI) Front‐end development (React) or backend development (.NET/C#) Commercial awareness and understanding of business value delivery #J-18808-Ljbffr ...

/CD pipelines, model versioning, and monitoring. \n Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. \n Proficiency in using containerization technologies (e.g., Docker ...

/CD pipelines, model versioning, and monitoring. Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. Proficiency in using containerization technologies (e.g., Docker) and orchestration tools ...

/CD pipelines, model versioning, and monitoring.\ NComprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions.\ NProficiency in usingcontainerization technologies (e.G., Docker) and orchestration tools (e.G. ...

/CD pipelines, model versioning, and monitoring. \n Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. \n Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

including CI/CD pipelines, model versioning, and monitoring.\ NExperience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio.\ NWorking knowledge ofcontainerization technologies (e.G., Docker) and orchestration tools (e.G., Kubernetes) to build and manage scalable AI solutions. ...

including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

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 ...

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 ...

including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud data and AI platforms ...

including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud data and AI platforms ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Platform & Cloud Engineering: Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Platform & Cloud Engineering: Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with ...

deployment, including MLOps, monitoring and drift detection. \ N\n\nPractical knowledge of ML frameworks (e.G. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \ N\nPlatform & Cloud Engineering: \ N\nArchitecture and delivery of scalable cloud data and AI platforms on AWS, Azure ...

tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation, A/B testing and online evaluation ...

Ecosystems: Solid understanding of ML algorithms, FastAPI, PyTorch/TensorFlow, MLflow, MLOps practices, containerization (Docker, Kubernetes/AWS EKS), and cloud services (AWS Bedrock, SageMaker, Azure AI Search). Soft Skills: Excellent communication and collaboration skills, with the ability to translate complex technical concepts for diverse, cross-functional stakeholders. ...

agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing, observability and production support. Modern ML/ ...

Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling. Experience integrating human-in-the-loop or user feedback signals into ...

developing a growing team of Data Scientists Establishing best practice across model development, governance and MLOps Tech Stack Python | SQL | TensorFlow | PyTorch | AWS | Azure | SageMaker | NLP | LLMs | MLOps What we're looking for: Strong commercial Data Science and Machine Learning experience Experience leading teams or acting as a technical ...

offs. Real-time ML & reproducibility: A strong grasp of production inference patterns and reproducible environments (Docker, MLflow or equivalent). Cloud & tooling: AWS, plus SageMaker, Snowflake, Spark/Databricks and Kubernetes. Engineering mindset: A focus on reliability, maintainability and continuous improvement. ...

frameworks (AutoPrompt, DSPy) and building evaluation suites. \n Hands-on experience with agentic frameworks (LangChain, LangGraph). \n Experience with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration. \n Demonstrated experience in financial services, particularly investment banking operations. \n\n Join us to help shape the future ...