1 to 25 of 40 Amazon SageMaker Jobs in the UK

with the expectation to be in the London office 1-2 times per week. Key Responsibilities Deploy ML models as real-time endpoints using Amazon SageMaker * Build and manage batch inference pipelines * Implement CI/CD workflows for ML using Git-based processes * Containerize applications using Docker * Monitor … CloudWatch * Automate data pipelines and feature workflows using Python & SQL * Ensure secure access and governance using AWS IAM and best practices Core AWS Stack Amazon SageMaker | Amazon S3 | Amazon Redshift | AWS Lambda | Amazon CloudWatch | AWS IAM What We're Looking For Strong hands-on experience ...

with the expectation to be in the London office 1-2 times per week. Key Responsibilities Deploy ML models as real-time endpoints using Amazon SageMaker * Build and manage batch inference pipelines * Implement CI/CD workflows for ML using Git-based processes * Containerize applications using Docker * Monitor … CloudWatch * Automate data pipelines and feature workflows using Python & SQL * Ensure secure access and governance using AWS IAM and best practices Core AWS Stack Amazon SageMaker | Amazon S3 | Amazon Redshift | AWS Lambda | Amazon CloudWatch | AWS IAM What We're Looking For Strong hands-on experience ...

with the expectation to be in the London office 1-2 times per week. Key Responsibilities Deploy ML models as real-time endpoints using Amazon SageMaker * Build and manage batch inference pipelines * Implement CI/CD workflows for ML using Git-based processes * Containerize applications using Docker * Monitor … CloudWatch * Automate data pipelines and feature workflows using Python & SQL * Ensure secure access and governance using AWS IAM and best practices Core AWS Stack Amazon SageMaker | Amazon S3 | Amazon Redshift | AWS Lambda | Amazon CloudWatch | AWS IAM What We're Looking For Strong hands-on experience ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (e.g. Azure Cognitive Services, AWS SageMaker, and/or GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (e.g. Jenkins, Docker, Kubernetes, and/ ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (e.g. Azure Cognitive Services, AWS SageMaker, and/or GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (e.g. Jenkins, Docker, Kubernetes, and/ ...

Knowledge graphs and semantic search Data engineering pipelines MLOps & Deployment CI/CD for ML, model monitoring, versioning Kubernetes, Docker, serverless architectures MLflow, Kubeflow, SageMaker or similar Cloud & Architecture Azure AI, AWS AI/Bedrock, Google Vertex AI API-led architectures and microservices Security, governance, and responsible AI Frameworks ...

secure, and cost‐efficient deployments using Docker, Kubernetes (AKS/EKS/GKE), and Terraform/Bicep. Lead enterprise MLOps capabilities using Azure ML, SageMaker, Vertex AI, MLflow, and Kubeflow, integrated with CI/CD (GitHub Actions, Azure DevOps, Jenkins, Argo CD). Oversee observability and monitoring using Prometheus ...

record building and maintaining CI/CD pipelines (GitHub Actions, CircleCI, Jenkins, GitLab CI) • Experience with MLOps and supporting ML model deployment workflows (AWS Sagemaker, Lambda, containerized deployments) • Proficiency in scripting and automation (Python, Bash, Go) • Strong experience with monitoring and observability tools (CloudWatch, Prometheus, Grafana, Datadog, Sentry ...

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 ...

adapters), RAG , and evaluation (golden sets, BLEU/BERTScore/semantic similarity). Cloud : Azure (incl. Azure AI, OpenAI, Cognitive Services ), AWS (Bedrock/SageMaker), GCP (Vertex AI); containerization (Docker/Kubernetes) and service meshes. Data & Integration : SQL, NoSQL, data warehousing concepts (fact/dimension, SCD), ETL/ ...

models using Python, PyTorch and modern ML frameworks Work with large datasets to train, evaluate and optimise models Implement and scale solutions on AWS (SageMaker, Bedrock, Lambda, etc.) Contribute to generative AI initiatives using Hugging Face and open-source models Collaborate with data engineers, architects and stakeholders to deliver ...

with LLMs/GenAI frameworks (LangChain, RAG, prompt engineering, etc.) Experience in cloud (AWS/Azure/GCP) Experience in MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML, etc.) API development experience Techno-Functional Capabilities Ability to translate business requirements into technical AI designs Strong solution architecture capability Experience designing ...

more cloud platforms or third-party technologies such as Google PaLM and Vertex AI, Azure OpenAI Services and Azure ML, AWS Bedrock and SageMaker, Dataiku, Databricks, Snowflake Snowpark and Snowflake Cortex, and DataRobot. Experience architecting scalable, high-performance, and cost-optimised AI and ML solutions using serverless technologies, containerised ...

Generative AI models, LLMs, and agentic frameworks. Collaborate with stakeholders to define problem statements and solution approaches. Deploy models using MLOps tools such as SageMaker, Snowflake, and CI/CD pipelines. Integrate vector databases and Retrieval-Augmented Generation (RAG) pipelines. Ensure high-quality delivery and adherence to best practices ...

Generative AI models, LLMs, and agentic frameworks. •Collaborate with stakeholders to define problem statements and solution approaches. •Deploy models using MLOps tools such as SageMaker, Snowflake, and CI/CD pipelines. •Integrate vector databases and Retrieval-Augmented Generation (RAG) pipelines. •Ensure high-quality delivery and adherence to best practices ...

native tooling. Architect and develop cutting-edge platform services for AI-driven trading. Tech Stack Programming: Python AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and Databricks On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. What we are looking for 5 – 10 years of experience in data engineering, ideally ...

similar OR packages Production ML/optimisation software experience - you've shipped models that run at scale Cloud platform experience (AWS preferred); familiarity with SageMaker, DVC, GitHub Actions a bonus Strong SQL and data engineering fundamentals Experience in airline, aviation, transport, or engineering/maintenance environments is highly desirable ...

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 ...

infrastructure, and cloud-native tooling. Tech Stack Programming: Expert level 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 ...

responsibilities: Design, develop, and implement automation solutions and AI-driven applications to optimise network control processes. Utilise AWS Bedrock, AWS Quicksuite, and AWS SageMaker, AWS Quicksuite and AWS Connect to build, deploy, and maintain scalable machine learning and automation pipelines. Collaborate with cross-functional teams, including operations, data science ...

pipelines for training, validation and deployment. Implementing CI/CD workflows for machine learning and maintaining reliable ML endpoints. Working heavily with AWS, including SageMaker, to deliver robust, secure and scalable ML infrastructure. Applying strong engineering standards across cloud, DevOps and automation practices. Contributing to computer vision and broader ...

Machine learning (supervised & unsupervised) Generative AI & LLMs Embeddings, vector databases, RAG MLOps and model lifecycle management Experience with platforms such as: Azure ML, AWS SageMaker, Google Vertex AI Databricks, LangChain, Hugging Face Ability to develop architecture artefacts (HLDs, solution diagrams, delivery models) Exceptional writing, storytelling, and stakeholder engagement skills ...

developing business intelligence solutions for reliable, seamless reporting to serve multiple stakeholders. The technology stack consists of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. Responsibilities Designing, building, and maintaining a Data Warehouse and related applications. Analysing, developing, delivering ...

developing business intelligence solutions for reliable, seamless reporting to serve multiple stakeholders. The technology stack consists of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. Responsibilities Designing, building, and maintaining a Data Warehouse and related applications. Analysing, developing, delivering ...

e.g. seaborn) Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g. MLflow) Experience with cloud-based ML tools (e.g. SageMaker), data and model versioning (e.g. DVC), CI/CD (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker ...