1 to 25 of 75 Permanent Amazon SageMaker Jobs in London

that fuel analytics, AI/ML, and data-driven operations.* Design and build high-performance data integration solutions: Use AWS Glue, AWS Step Functions, Amazon EventBridge, and AWS Database Migration Service (DMS) to orchestrate ingestion, transformation, and movement of data across on-premises and cloud sources into Amazon … Design and oversee the delivery of secure lakehouse and warehouse architectures: Implement logical and physical data models, data quality controls, and governance practices using Amazon S3 (data lake), AWS Lake Formation, Amazon Redshift, and Amazon Athena to ensure trusted data.* Ability to design AI/ML-enabled ...

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 ...

containerization technologies like docker, Kubernetes, AWS EKS etc.Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g. AWS Bedrock,AWS S3, AWS Sagemaker, Azure AI search, Azure OpenAI, Azure blob storage etc.Master's degree or above in Machine learning/data science, computer science, applied mathematics ...

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). Platform & Cloud Engineering: Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with ...

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 ...

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). Platform & Cloud Engineering: Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with ...

frameworks.Inference & 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 frameworks ...

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 ...

functional, and globally distributed teams. Continuous learning mindset with a focus on emerging technologies and innovation. 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 ...

training,evaluationand production deployment, includingMLOps, 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). Platform & Cloud Engineering Architecture and delivery of scalable cloud data and AI platforms on AWS,Azureor GCP; experienced with ...

with Cloud services, Infrastructure as Code (IaC) and containerized application development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps) Experience working on AIML systems and/ ...

. Hands‐on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud‐native AI/ML platforms (e.g. AWS SageMaker, AzureML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK). Solid understanding of MLOps – CI/CD, IaC (Terraform), experiment tracking, model registry ...

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

good grasp of data engineering lifecycles, ML Ops and LLM Ops.Desirable:Experience with/in: Experience with other cloud provider ML and LLM services (sagemaker, bedrock, langchain, llamaindex, boto3 etc.)Experience with image analysis and extraction tools (Google Cloud Vision, Document Intelligence, MarkItDown)Knowledge of Agentic protocols and their ...

Proficiency in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI)What we offerCulture of caring. At GlobalLogic, we prioritize a culture of caring. Across every ...

Kubernetes).Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines).Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI)Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment.Excellent communication and a drive to work ...

Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines). Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI) Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment. Excellent communication and a drive ...

scikit, pytorch, tidyverse, caret, ggplot, etc.).Proven experience developing, refining, and monitoring NLP models.Familiarity with database management tools and techniques (e.g., SQL, Selenium, S3, Sagemaker, API protocols) is preferred but not required.Understanding model evaluation methods and metrics.Ability to operationalize non-technical ideas into relevant research designs, features, and model ...

preferred). Hands-on experience with GCP Vertex AI(model endpoints, pipelines, embeddings, vector search)or equivalent cloud-nativeAI/ML platforms (e.g. AWS SageMaker, AzureML) andagentorchestration frameworks(e.g.LangChain,LangGraph, ADK). Solid understanding ofMLOps - CI/CD,IaC(Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying ...

equivalents in Azure or GCP), with hands‐on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock. What you’ll doPartner with stakeholders across the business to explore high‐impact opportunities.Own the full lifecycle: problem framing, data discovery, feature engineering … methods, and building RAG systems.Orchestration: for example LangChain for pipelines/agents. RAG best practices and evaluation workflows (e.g., agentic/RAG patterns on SageMaker). Comfortable choosing the right technique for the job (from baselines to advanced models), with an emphasis on measurable impact and maintainability.Clear communication with ...

equivalents in Azure or GCP), with hands‐on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock. What you’ll do Partner with stakeholders across the business to explore high‐impact opportunities. Own the full lifecycle: problem framing, data discovery … building RAG systems. Orchestration: for example LangChain for pipelines/agents. RAG best practices and evaluation workflows (e.g., agentic/RAG patterns on SageMaker). Comfortable choosing the right technique for the job (from baselines to advanced models), with an emphasis on measurable impact and maintainability. Clear communication with ...

NeedMLOps experience: You’ve operationalised ML models in production, owning deployment, monitoring and lifecycle management.Strong programming: Production-quality, testable Python.Cloud expertise: Deep AWS knowledge (SageMaker, Lambda, ECS/EKS, Step Functions); Snowflake a plus.MLOps tooling: Experiment tracking and registries, workflow orchestration, model serving and feature stores.CI/ ...

strong positive signal. Hands-on production experience with graph neural networks, anomaly detection, or behavioural biometrics, beyond prototyping or fine-tuning. Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices. Experience mentoring or technically guiding other data scientists. Please include a CV in English. Curious ...

Familiarity with prompt optimization frameworks (AutoPrompt, DSPy) and building evaluation suites.Hands-on experience with agentic frameworks (LangChain, LangGraph).Experience with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration.Demonstrated experience in financial services, particularly investment banking operations.Join us to help shape the future of Market Operations through innovative ...

SkillsExperience with Cloud services, Infrastructure as Code (IaC) and containerized application developmentFamiliarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and BedrockPractical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps)Experience working on AIML systems and/or prior ...