7 of 7 Remote/Hybrid Amazon SageMaker Jobs in the UK excluding London

have: Strong Python ML experience (e.g. scikit-learn, XGBoost, PyTorch, or TensorFlow) Production experience with AWS ML services (e.g. SageMaker, Lambda, S3) Proven ability to move models from experimentation into live systems Experience with MLOps tools (MLflow, Weights & Biases, DVC, or similar) Hands-on exposure to LLM/GenAI ...

Computer Science, Machine Learning, Engineering , or a closely related discipline. Bonus Points Experience with MLOps and model serving platforms such as BentoML, MLflow, or SageMaker . Hands-on work with streaming and batch data pipelines using tools like Spark, Airflow, or Apache Beam . Background in front ...

engineering with GitHub Actions and/or Argo CD. Preferred/Bonus Research experience in edge AI or constrained/offline deployments. MLOps experience (Sagemaker, Kubeflow, ZenML). Experience building RESTful services around AI pipelines. ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. Experience with AWS Karpenter ...

similar large-scale ranking models. Generative AI & Technical Proficiency: Hands-on experience with LLM fine-tuning, Python, modern NLP frameworks, and familiarity with AWS SageMaker, Docker, and Airflow. Strong mathematical and statistical foundation. Ethical Drive: Commitment to ethical and fair AI practices that ensureequitableoutcomes for all users. Additional Information ...

runtime environments. Work closely with data engineering, platform engineering, security, MLOps, and Independent Model Monitoring (IMM) teams. Contribute to integration efforts with AWS SageMaker, model pipelines, and enterprise data platforms. Use technologies such as AWS, SageMaker, Python, Java, Kafka, Open Telemetry, and cloud-native monitoring stacks. Support governance … data platforms. Proficiency in Python, SQL, Java , and modern data processing frameworks. Experience working with cloud-based AI/ML ecosystems , particularly AWS SageMaker (required). Understanding of monitoring frameworks , observability pipelines, and dashboards. Familiarity of event-driven architectures and messaging systems (Kafka, Vert.x, or similar). Knowledge ...

validation. Own observability (Datadog), defining SLIs/SLOs, dashboards, alerting, MS Teams integrations, and telemetry based improvements. Drive AI enabled operations using Bedrock/SageMaker and the Model Context Protocol (MCP) for log analysis, anomaly detection, and incident workflows. Define cloud guardrails and enforce security controls, including IAM boundaries … desired experience includes: Experience with AI driven operational tooling (triage, runbooks, change management) with strong governance controls. Supporting large scale AI/ML workloads — SageMaker, GPU autoscaling, Kubernetes based model serving. Experience implementing policy as code (OPA) Experience implementing compliance frameworks (CIS, NIST, ISO 27001). Applying AI within ...

systems across the bank-model registries, feature stores, lineage services, governance controls, dashboards, and reporting pipelines. Work directly with technologies such as AWS SageMaker, Python, SQL, Java, event-driven systems, Open Telemetry, Kafka, and cloud-native monitoring stacks. Support governance of AI systems through transparent metrics, automated reporting … systems or data platforms. Fluency in Python, SQL, Java , and modern data processing frameworks. Expertise in cloud-based AI/ML ecosystems , particularly AWS SageMaker (required). Proven experience developing monitoring frameworks , observability pipelines, and dashboards. Deep understanding of event-driven architectures and messaging systems (Kafka, Vert.x, or similar ...