1 to 25 of 61 Amazon SageMaker Jobs in the UK

including model hosting, request routing, continuous batching, and KV‐cache optimization Deploy, host, and lifecycle‐manage open‐source and proprietary LLMs on Amazon EKS and Amazon SageMaker, as well as on‐prem and local GPU clusters, using reproducible infrastructure as code and continuous delivery pipelines Implement observability … experience operating LLM inference servers such as vLLM and llm‐d (or directly equivalent serving stacks) Hands‐on experience hosting and serving LLMs on Amazon EKS and/or Amazon SageMaker, and on local GPU infrastructure Knowledge of LLM reliability and risk considerations, including latency/throughput ...

delivery of a production-grade machine learning platform on AWS. What you’ll be doing Design and maintain a production MLOps platform on Amazon SageMaker (Studio, Training, Pipelines, Endpoints) — including model registry, automated retraining, drift monitoring, and governance gates Lead the migration of a 12-model production suite … e.g., the CVM suite) from legacy infrastructure to SageMaker, owning parity testing methodology and sign-off Build and maintain CI/CD pipelines (CodePipeline/CodeBuild or equivalent) for automated model promotion across environments Define and enforce IAM least-privilege policies, KMS key management, and VPC/PrivateLink network ...

Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within: Microsoft Azure Google Cloud Platform (GCP) Additional experience with: Docker Kubernetes Git CI/CD Pipelines ...

cost-effective deployment via Docker , Kubernetes (AKS/EKS/GKE) , and Terraform/Bicep . Lead enterprise MLOps initiatives using Azure ML , SageMaker , Vertex AI , MLflow , and Kubeflow , with continuous integration pipelines (GitHub Actions, Azure DevOps, Jenkins, Argo CD). Oversee monitoring and observability using Prometheus , Grafana ...

distributed teams Continuous learning mindset with a focus on emerging technologies and innovation Nice-to-Have Experience with AWS or GCP ML platforms (e.g., SageMaker, Vertex AI) Front‐end development (React) or backend development (.NET/C#) Commercial awareness and understanding of business value delivery #J-18808-Ljbffr ...

serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with containerisation, IaC, event ...

including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering: ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms: AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps: Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering: ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms: AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps: Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering: ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms: AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps: Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering: ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms: AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps: Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering: ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms: AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps: Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering : ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms : AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps : Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering : ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms : AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps : Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering : ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms : AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps : Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering : ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms : AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps : Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation, A/B testing and online evaluation ...

using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including version control, testing, and API/microservices architecture. Experience with MLOps platforms (e.g., MLflow, SageMaker) or agentic AI frameworks. Familiarity with secure development practices or regulated/high-compliance environments. Background in NLP, computer vision, or data engineering platforms. ...

agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing, observability and production support. Modern ML/ ...

Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling. Experience integrating human-in-the-loop or user feedback signals into ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands-on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML) Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

developing a growing team of Data Scientists Establishing best practice across model development, governance and MLOps Tech Stack Python | SQL | TensorFlow | PyTorch | AWS | Azure | SageMaker | NLP | LLMs | MLOps What we're looking for: Strong commercial Data Science and Machine Learning experience Experience leading teams or acting as a technical ...

microservices and working with event-driven architectures. Proficiency in designing and consuming RESTful and GraphQL APIs. Experience with AWS or Azure ML services (e.g., SageMaker, Bedrock, Azure ML) and cloud-native development. Experience with PostgreSQL and MongoDB or similar relational and NoSQL databases. A solid grasp of system design ...

microservices and working with event-driven architectures. Proficiency in designing and consuming RESTful and GraphQL APIs. Experience with AWS or Azure ML services (e.g., SageMaker, Bedrock, Azure ML) and cloud-native development. Experience with PostgreSQL and MongoDB or similar relational and NoSQL databases. A solid grasp of system design ...