1 to 25 of 29 Amazon SageMaker Jobs in the North of England

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

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

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

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

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

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

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

APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments. Knowledge of data privacy, accessibility ...

APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments. Knowledge of data privacy, accessibility ...

APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments. Knowledge of data privacy, accessibility ...

APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments. Knowledge of data privacy, accessibility ...

APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments. Knowledge of data privacy, accessibility ...

APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments. Knowledge of data privacy, accessibility ...

APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments. Knowledge of data privacy, accessibility ...

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 ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

/Knowledge Graph expertise - deep knowledge with Knowledge Graphs and vector databases Strong technical fluency - TypeScript/React, Langgraph/Langchain, neo4j, Docker, AWS (SageMaker, Bedrock, Hugging face), to name a few So What's Next? If you are an experienced AI Software developer with Knowledge Graph expertise ...