1 to 25 of 80 Amazon SageMaker Jobs in the UK excluding London

pipelines and ETL processes using state-of-the-art tools Skilled at architecting and managing secure, scalable AWS environments and working experience in the data & analytics services such as Amazon EC2, AWS Lambda, AWS Fargate, Amazon ECS, Amazon EKS, Amazon S3, AWS Glue, Amazon RDS, Amazon DynamoDB, Amazon Aurora, Amazon SageMaker, Amazon Bedrock (including LLM hosting and management). Expertise in workflow orchestration tools such as Apache Airflow Experience implementing DataOps best practices and tooling, including DataOps.Live Advanced skills in data storage and management platforms like Snowflake Ability to deliver insightful analytics via business intelligence tools such as Power BI Full-stack development experience: backend (Node.js, Python), frontend (ReactJS … Demonstrated experience designing and implementing Generative AI solutions (chatbots, digital assistants, content generation, etc.) Hands-on implementation and operation of AI/ML models with services like Amazon SageMaker Advanced proficiency in Python and related AI/ML productivity libraries Expertise in SQL and NoSQL database technologies Skills Python AWS Gen AI Job Title: Technical Lead Location: Cambridge More ❯

SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda, SageMaker), Kubernetes, Terraform/CDK ML Ops & Automation: MLFlow, feature stores, model monitoring, A/B testing Data Security & Compliance: Secure SDLC, ISO/NIST, data governance, GDPR-compliant pipelines More ❯

SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda, SageMaker), Kubernetes, Terraform/CDK ML Ops & Automation: MLFlow, feature stores, model monitoring, A/B testing Data Security & Compliance: Secure SDLC, ISO/NIST, data governance, GDPR-compliant pipelines More ❯

SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda, SageMaker), Kubernetes, Terraform/CDK ML Ops & Automation: MLFlow, feature stores, model monitoring, A/B testing Data Security & Compliance: Secure SDLC, ISO/NIST, data governance, GDPR-compliant pipelines More ❯

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

Hugging Face, LangChain/LangGraph, LlamaIndex). Practical experience implementing CI/CD pipelines using tools like GitHub Actions or Jenkins, and managing MLOps and LLMOps with MosiacAI, MLflow, Sagemaker or similar platforms. Design and develop lightweight, interactive user interfaces using frameworks such as Streamlit, Flask, or similar tools to support stakeholder and client demonstrations. Led technical due diligence More ❯

Hugging Face, LangChain/LangGraph, LlamaIndex). Practical experience implementing CI/CD pipelines using tools like GitHub Actions or Jenkins, and managing MLOps and LLMOps with MosiacAI, MLflow, Sagemaker or similar platforms. Design and develop lightweight, interactive user interfaces using frameworks such as Streamlit, Flask, or similar tools to support stakeholder and client demonstrations. Led technical due diligence More ❯

with Python – essential. Has hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs to both technical and More ❯

with Python – essential. Has hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs to both technical and More ❯

with Python – essential. Has hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs to both technical and More ❯

to Artificial Intelligence, Machine Learning, or Natural Language Processing Familiarity with frameworks like Express.js, Flask, or NestJS Familiarity with libraries like TensorFlow, PyTorch, or scikit-learn Basic understanding of Amazon Web Services Culture: Coffee and snacks in the office Team socials Inclusive working environment, supporting all genders, sexualities, race, disability or background Join our dynamic team at Revenir and More ❯

to Artificial Intelligence, Machine Learning, or Natural Language Processing Familiarity with frameworks like Express.js, Flask, or NestJS Familiarity with libraries like TensorFlow, PyTorch, or scikit-learn Basic understanding of Amazon Web Services Culture: Coffee and snacks in the office Team socials Inclusive working environment, supporting all genders, sexualities, race, disability or background Join our dynamic team at Revenir and More ❯

MLOps, Data Scientist, Artificial Intelligence, AWS, Terraform, Kubernetes, EKS, Docker, Containerisation, Python, Go, Bash, CI/CD, GitHub Actions, Argo CD, Edge AI, Offline AI, MOD Clearance, Security Clearance, Sagemaker, Kubeflow, ZenML, AI Architect, Solution Architect. More ❯

generation (RAG). Strong grasp of software engineering principles (SOLID, design patterns), dependency injection, and scalable system design. Hands on experience with ML tooling such as MLflow, Snowflake, Databricks, SageMaker or Azure Machine Learning. Hands on production experience deploying and monitoring AI/ML systems on cloud platforms such as AWS, GCP or Azure BONUS SKILLS/EXPERIENCE: Contributions More ❯

science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to More ❯

implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and cloud platforms: Google Vertex AI, AWS SageMaker, or Azure AI. Knowledge of AI security, model explainability, and Responsible AI principles. Experience building or integrating voice-enabled chatbots and multi-modal systems. Experience working with open-source More ❯

implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and cloud platforms: Google Vertex AI, AWS SageMaker, or Azure AI. Knowledge of AI security, model explainability, and Responsible AI principles. Experience building or integrating voice-enabled chatbots and multi-modal systems. Experience working with open-source More ❯

implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and cloud platforms: Google Vertex AI, AWS SageMaker, or Azure AI. Knowledge of AI security, model explainability, and Responsible AI principles. Experience building or integrating voice-enabled chatbots and multi-modal systems. Experience working with open-source More ❯

. - Solid CI/CD engineering with GitHub Actions and/or Argo CD. Preferred/Bonus - Research experience in edge AI or constrained/offline deployments. - MLOps experience (Sagemaker, Kubeflow, ZenML). - Experience building RESTful services around AI pipelines. - ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. - Experience with AWS Karpenter, Prometheus, or similar observability stacks. More ❯

. - Solid CI/CD engineering with GitHub Actions and/or Argo CD. Preferred/Bonus - Research experience in edge AI or constrained/offline deployments. - MLOps experience (Sagemaker, Kubeflow, ZenML). - Experience building RESTful services around AI pipelines. - ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. - Experience with AWS Karpenter, Prometheus, or similar observability stacks. More ❯

. - Solid CI/CD engineering with GitHub Actions and/or Argo CD. Preferred/Bonus - Research experience in edge AI or constrained/offline deployments. - MLOps experience (Sagemaker, Kubeflow, ZenML). - Experience building RESTful services around AI pipelines. - ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. - Experience with AWS Karpenter, Prometheus, or similar observability stacks. More ❯

Python Experience with machine learning, familiar with Huggingface, Pytorch, and similar ML tools and packages Familiarity with deploying and scaling ML models in the cloud, particularly with AWS and SageMaker Understanding of DevOps processes and tools: CI/CD, Docker, Terraform, and monitoring/observability Bonus: experience with vector databases, semantic search, or event-driven systems like Kafka Additional More ❯

decisioning is strongly preferred Experience with model governance and monitoring in regulated environments Experience with cloud platforms (AWS, GCP, Azure), preferably AWS, ML tools such as the AWS suite: Sagemaker Key Details Reporting to Lead Data Scientist Hours Full time Location London - Hybrid WFH model, x2 days a week onsite (Wed/Thurs) Working with Yaspa We are a More ❯