1 to 25 of 83 Permanent Amazon SageMaker Jobs in England

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 ❯

ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end 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 ❯

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 ❯

experience with cloud-native development (GCP preferred). Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks such as LangChain and LangGraph Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying 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 ❯

or internal clients within large organisations) through RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools More ❯

or internal clients within large organisations) through RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools More ❯

or internal clients within large organisations) through RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools 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 ❯

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 ❯

developing and deploying ML models in production environments. Strong Python skills, with experience in frameworks like PyTorch, TensorFlow, or Hugging Face. Confident working in AWS or similar cloud environments (SageMaker, Lambda, Docker, etc.). Experienced in (or eager to explore) areas such as forecasting, optimisation, reinforcement learning, generative AI, or computer vision. Solid engineering mindset, you know how to More ❯

services. Hands on experience with DevOps and engineering tools (GitLab, GitHub, Azure DevOps, Docker, Kubernetes). Proficiency with AI/ML and MLOps platforms (Databricks, Google Cloud Vertex AI, SageMaker). Familiarity with generative AI technologies and frameworks (OpenAI, Google Gemini, Hugging Face Transformers). Demonstrated success in developing and executing product strategies. Ability to lead and inspire cross 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 ❯

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 ❯

. - 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 ❯

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 ❯