26 to 50 of 88 Amazon SageMaker Jobs in London

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 ❯

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 ❯

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 ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Dfferent Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Dfferent Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Different Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Different Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Different Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Different Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

to build scalable, production-grade AI systems. Preferred qualifications: Advanced degree (MSc/PhD) in Computer Science, Data Science, or a related STEM field. Experience with Azure Foundry, AWS SageMaker, or other model-serving tools. Prior exposure to SaaS, enterprise, or data-rich product environments. Why This Role Play a central role in defining and executing an organisation-wide More ❯

to build scalable, production-grade AI systems. Preferred qualifications: Advanced degree (MSc/PhD) in Computer Science, Data Science, or a related STEM field. Experience with Azure Foundry, AWS SageMaker, or other model-serving tools. Prior exposure to SaaS, enterprise, or data-rich product environments. Why This Role Play a central role in defining and executing an organisation-wide More ❯

to build scalable, production-grade AI systems. Preferred qualifications: Advanced degree (MSc/PhD) in Computer Science, Data Science, or a related STEM field. Experience with Azure Foundry, AWS SageMaker, or other model-serving tools. Prior exposure to SaaS, enterprise, or data-rich product environments. Why This Role Play a central role in defining and executing an organisation-wide More ❯

to build scalable, production-grade AI systems. Preferred qualifications: Advanced degree (MSc/PhD) in Computer Science, Data Science, or a related STEM field. Experience with Azure Foundry, AWS SageMaker, or other model-serving tools. Prior exposure to SaaS, enterprise, or data-rich product environments. Why This Role Play a central role in defining and executing an organisation-wide More ❯

Looking For Must Have Practical experience deploying ML models into production environments Strong Python development skills and understanding of ML model structures Familiarity with tools such as MLflow, Airflow, SageMaker, or Vertex AI Understanding of CI/CD concepts and basic infrastructure automation Ability to write well-tested, maintainable, and modular code Strong collaboration skills and a growth mindset More ❯

HIPAA) Excellent communication, client engagement, and workshop facilitation skills Proven ability to work in matrix environments across global teams Desired skills Exposure to AI platforms like Azure AI, AWS SageMaker, Google Vertex AI Knowledge of PoC packaging and offer development for enterprise clients Experience Experience working with AI CoEs or global delivery teams Benefits Collaborative working environment - we stand More ❯

Proven experience in solution design, technical consulting, or pre-sales Ability to communicate complex concepts clearly to varied audiences Familiarity with cloud AI environments such as Azure AI, AWS Sagemaker, or GCP Vertex A natural problem solver with curiosity for cutting-edge tools and frameworks Desirable Experience Experience delivering AI or ML solutions in a commercial or consultancy setting More ❯

Proven experience in solution design, technical consulting, or pre-sales Ability to communicate complex concepts clearly to varied audiences Familiarity with cloud AI environments such as Azure AI, AWS Sagemaker, or GCP Vertex A natural problem solver with curiosity for cutting-edge tools and frameworks Desirable Experience Experience delivering AI or ML solutions in a commercial or consultancy setting More ❯

Proven experience in solution design, technical consulting, or pre-sales Ability to communicate complex concepts clearly to varied audiences Familiarity with cloud AI environments such as Azure AI, AWS Sagemaker, or GCP Vertex A natural problem solver with curiosity for cutting-edge tools and frameworks Desirable Experience Experience delivering AI or ML solutions in a commercial or consultancy setting More ❯

Proven experience in solution design, technical consulting, or pre-sales Ability to communicate complex concepts clearly to varied audiences Familiarity with cloud AI environments such as Azure AI, AWS Sagemaker, or GCP Vertex A natural problem solver with curiosity for cutting-edge tools and frameworks Desirable Experience Experience delivering AI or ML solutions in a commercial or consultancy setting More ❯

with frameworks like Airflow, Spark, or similar Solid understanding of data modelling, architecture, and warehousing best practices Comfort working with cloud platforms (AWS, Azure) and infrastructure tools (e.g. Terraform, SageMaker) Willingness to gain intermediate-level backend development skills (especially with Python/Django) Bonus: experience with data lakes, CI/CD, and data quality/versioning tools If this More ❯

with frameworks like Airflow, Spark, or similar Solid understanding of data modelling, architecture, and warehousing best practices Comfort working with cloud platforms (AWS, Azure) and infrastructure tools (e.g. Terraform, SageMaker) Willingness to gain intermediate-level backend development skills (especially with Python/Django) Bonus: experience with data lakes, CI/CD, and data quality/versioning tools If this More ❯

Docker, Git, cloud networking, and cloud security for services and infrastructure. Experience with Kubernetes (K8S) is a plus. - Cloud Expertise: Familiarity with AI infrastructure on AWS and GCP, including Sagemaker, Vertex, Triton, and GPU computing. - Bonus Points: Experience with Airbyte is a significant advantage. Perks and Benefits: - Hybrid/Remote Option: Freedom to work from anywhere in the world More ❯

Docker, Git, cloud networking, and cloud security for services and infrastructure. Experience with Kubernetes (K8S) is a plus. - Cloud Expertise: Familiarity with AI infrastructure on AWS and GCP, including Sagemaker, Vertex, Triton, and GPU computing. - Bonus Points: Experience with Airbyte is a significant advantage. Perks and Benefits: - Hybrid/Remote Option: Freedom to work from anywhere in the world More ❯

integration. Familiarity with CI/CD pipelines and modern DevOps workflows. Understanding of authentication, authorization, and web security best practices. Nice-to-haves: Experience deploying ML models using AWS Sagemaker, Hugging Face, or Replicate . Familiarity with WebGL, THREE.js , or other 3D graphics frameworks. Interest in AI, generative models, and spatial computing . More ❯

scale, integrating them with enterprise workflows, and ensuring repeatable, cost-efficient AWS architectures. Responsibilities: Leading solution workshops to design scalable ML systems on AWS using services like VPC, IAM, SageMaker Studio, Lambda, and EKS You'll build CI/CD pipelines using GitHub Actions, Jenkins, and AWS CodePipeline for deploying traditional ML, GenAI models, and AI agents Deploying LLMs … using tools like LangChain, LangGraph, and custom orchestrators Your expertise will help reduce cloud costs with GPU acceleration, auto-scaling, and spot instances To implement model lifecycle tools (MLflow, SageMaker Registry), performance dashboards, alerts, and automated retraining pipelines Connecting ML models to client systems using APIs, Kafka, and build agent workflows with vector databases (Pinecone, Weaviate) You'll enforce … or in a directly related field (2.1 min grade) 3+ years in MLOps/ML Engineering experience, plus 5+ years in Python software development or data science Skilled in SageMaker (training, endpoints, pipelines), Lambda, Step Functions, S3, and CloudWatch Proficiency with Terraform or AWS CDK, Docker, and Kubernetes (EKS/Fargate) Experienced with MLflow (or alternatives), GitHub Actions, Jenkins More ❯