6 of 6 Permanent Amazon SageMaker Jobs in London

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). Software Engineering: Production-grade Python services (FastAPI, AWS Lambda, event-driven patterns) and front-end development with React/Next.js ...

learning solutions using Python. Experience with frameworks such as: PyTorch TensorFlow Scikit-learn XGBoost Experience deploying ML solutions on AWS, including services such as SageMaker, Lambda, and S3. Strong understanding of MLOps tooling, including MLflow, Weights & Biases, or Data Version Control (DVC). Experience developing LLM or Generative … agents and agentic workflows. Vector databases such as Pinecone, Weaviate, or pgvector. Feature stores including Feast or AWS Feature Store. Docker, Kubernetes, or Amazon ECS. Infrastructure as Code using Terraform or CloudFormation. Big data processing technologies such as Spark or Dask. Experience working within regulated or highly secure environments. ...

models influence real lending decisions and commercial outcomes Experience deploying and maintaining models within production environments Strong Python, SQL and AWS experience (particularly SageMaker and Lambda) Comfortable working in a fast-moving, hands-on environment with high ownership Excellent communication skills with both technical and business stakeholders What ...

your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that … Analytics, Database, and/or Storage- PhD in computer science, machine learning, engineering, or related fields, or experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2- Knowledge of general AI tools- Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms ...

success metrics.- Position AI and GenAI as the outcome layer of migration: help customers see how moving to AWS unlocks access to services like Amazon Bedrock, SageMaker, and partner AI technologies (e.g. Anthropic Claude) to drive competitive advantage post-migration.- Own revenue targets across the full transformation lifecycle. … career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that ...

dashboards, alerting, and MS Teams integrations—and reduce incidents via telemetry-driven automation and blameless postmortems. Champion AI‐enabled operations using AWS Bedrock/SageMaker and Model Context Protocol (MCP) for log analysis, anomaly detection, incident triage, and workflow orchestration; establish adoption guardrails. Define and enforce cloud guardrails … operations—incident triage, runbooks, and change management—with clear guardrails, auditability, and human-in-the-loop controls. Supporting AI/ML workloads at scale: SageMaker endpoints, GPU node groups, autoscaling, and Kubernetes-based model serving. Policy-as-code (OPA) and compliance implementation across CIS, NIST, ISO 27001, with automated ...