8 of 8 Remote/Hybrid Amazon SageMaker Jobs in the UK

model choices to technical and non-technical stakeholders Develop clean, well-tested, production-ready Python code Build, train and deploy models on AWS using Amazon SageMaker and related services Tune model performance through hyperparameter optimisation, feature engineering and error analysis Instrument models in production, defining and tracking MLOps … learn), writing maintainable, production-quality code. AWS: Hands-on experience building, training and deploying ML models on the AWS stack, ideally with experience in Amazon SageMaker Model tuning & MLOps: Practical experience in hyperparameter tuning and performance optimisation. You can articulate the metrics you'd track for MLOps (e.g. ...

model choices to technical and non-technical stakeholders Develop clean, well-tested, production-ready Python code Build, train and deploy models on AWS using Amazon SageMaker and related services Tune model performance through hyperparameter optimisation, feature engineering and error analysis Instrument models in production, defining and tracking MLOps … learn), writing maintainable, production-quality code. AWS: Hands-on experience building, training and deploying ML models on the AWS stack, ideally with experience in Amazon SageMaker Model tuning & MLOps: Practical experience in hyperparameter tuning and performance optimisation. You can articulate the metrics you'd track for MLOps (e.g. ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

calibration) • Guardrail and AI-observability tooling (e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust) MLOps & LLMOps • Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker) and vector/retrieval databases (Pinecone, Milvus, pgvector) • Experience with GPU-accelerated infrastructure and NVIDIA AI Enterprise or equivalent stacks • Exposure to fine-tuning ...

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 ...

libraries, frameworks and services : such as ML libraries e.g. TensorFlow or PyTorch; model servers such as AWS Bedrock; MLOps such as ML Flow or Sagemaker; agent/AI orchestration such as LangGraph or MCP libraries Strong programming skills, especially in Python (other languages like C++, Scala, Rust ...