1 to 25 of 37 Remote Amazon SageMaker Jobs in the UK

CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent More ❯

CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent More ❯

the RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure More ❯

a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs More ❯

a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs More ❯

a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs More ❯

monitoring, and performance optimisation of ML systems Contribute to architectural discussions and promote engineering best practices Technical Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track More ❯

monitoring, and performance optimisation of ML systems Contribute to architectural discussions and promote engineering best practices Technical Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track More ❯

and fine‑tuning foundation‑model solutions; RAG patterns; prompt‑engineering & evaluation MLOps/LLMOps: CI/CD for models, feature stores, experiment tracking (MLflow, SageMaker, Azure ML, Vertex, etc.) Cloud‑Native Delivery: Building scalable AI pipelines on AWS or Azure; Terraform/IaC familiarity Python Excellence: Clean, testable code More ❯

and unsupervised learning techniques Experience in demand prediction, optimisation, or computer vision is advantageous Comfortable working with cloud platforms (preferably AWS) and services like SageMaker or Lambda Strong mathematical and statistical foundations, with a sharp eye for patterns and insights Willingness to build basic backend development skills (Python/ More ❯

such as Power BI and OAS. Understanding of data replication tools such as Oracle GoldenGate and AWS DMS. Knowledge of AWS Services such as SageMaker, Lambda, Step Functions, S3 etc would be an advantage. Hands on and technical understanding of complex Data Warehousing terminology. Python scripting knowledge would be More ❯

such as Power BI and OAS Understanding of data replication tools such as Oracle GoldenGate and AWS DMS Knowledge of AWS Services such as SageMaker, Lambda, Step Functions, S3 etc would be an advantage Hands on and technical understanding of complex Data Warehousing terminology. Python scripting knowledge would be More ❯

such as Power BI and OAS Understanding of data replication tools such as Oracle GoldenGate and AWS DMS Knowledge of AWS Services such as SageMaker, Lambda, Step Functions, S3 etc would be an advantage Hands on and technical understanding of complex Data Warehousing terminology. Python scripting knowledge would be More ❯

business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages More ❯

business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages More ❯

Experience across cloud platforms (AWS, Azure, GCP), with DevOps/ML Ops tools (eg Terraform, Docker, Kubernetes) Expertise with data science platforms like Databricks, SageMaker, or AzureML Excellent stakeholder engagement and communication skills Proven experience managing teams and delivering complex projects Security Requirements You must be eligible for Developed More ❯

data versioning, quality management, and CI/CD pipelines Experience with cloud platforms (e.g., AWS or Azure) and data tools such as Terraform or SageMaker is a plus Ideally, some hands-on experience building and maintaining data pipelines in a production environment What's on Offer: Competitive salary More ❯

analytical person with a bias for action. What Technologies We Use: Python, dbtLabs, SQL, GitHub Actions (CI/CD) Terraform, Docker, MLOps Platform (VertexAI, Sagemaker and MLFlow) With ecobee, you'll have the opportunity to: Be part of something big: Get to work in a fresh, dynamic, and ever More ❯

/21, FCA PS21/3, DORA, NIST AI RMF). Experience with AI/ML platforms and monitoring tools (e.g., MLflow, Azure ML, SageMaker, Python). Excellent stakeholder management and communication skills. Desirable Skills: Technical background in AI/ML, data science, or software engineering. Experience with cloud More ❯

/21, FCA PS21/3, DORA, NIST AI RMF). Experience with AI/ML platforms and monitoring tools (e.g., MLflow, Azure ML, SageMaker, Python). Excellent stakeholder management and communication skills. Desirable Skills: Technical background in AI/ML, data science, or software engineering. Experience with cloud More ❯

/21, FCA PS21/3, DORA, NIST AI RMF). Experience with AI/ML platforms and monitoring tools (e.g., MLflow, Azure ML, SageMaker, Python). Excellent stakeholder management and communication skills. Desirable Skills: Technical background in AI/ML, data science, or software engineering. Experience with cloud More ❯

/21, FCA PS21/3, DORA, NIST AI RMF). Experience with AI/ML platforms and monitoring tools (e.g., MLflow, Azure ML, SageMaker, Python). Excellent stakeholder management and communication skills. Desirable Skills: Technical background in AI/ML, data science, or software engineering. Experience with cloud More ❯

/21, FCA PS21/3, DORA, NIST AI RMF). Experience with AI/ML platforms and monitoring tools (e.g., MLflow, Azure ML, SageMaker, Python). Excellent stakeholder management and communication skills. Desirable Skills: Technical background in AI/ML, data science, or software engineering. Experience with cloud More ❯

grow a diverse team who are geographically dispersed. Comfortable using a multi-platform stack to deliver products (Databricks, Azure ML Studio, Azure and Snowflake, SageMaker, AWS). Ability to take ownership of a broad data product-set and drive forward the delivery of new products and product features. Strong More ❯

deployed applications. Package and deploy ML models (including Hugging Face Transformers) as microservices on AWS. Build, train, and evaluate models using AWS tools like SageMaker, Bedrock, Glue, Athena, and Redshift. Develop secure APIs with Apigee and build automation pipelines using Jenkins, Maven, and more. Support the full ML lifecycle … with MLOps , from experimentation to deployment and monitoring. Strong grasp of ethical AI practices: model explainability, transparency, bias mitigation. Hands-on with AWS services (SageMaker, Glue, Redshift, Bedrock, Lambda, Fargate). Skilled in deploying and hosting microservices/APIs with Flask or FastAPI. Proficient in SQL, Git, Jupyter/… collaborating in a dynamic environment Tech Stack Languages & Frameworks: Python, R, SQL, Flask, FastAPI Tools: Hugging Face, Apigee, Jenkins, Git, RStudio, Jupyter Cloud: AWS (SageMaker, Glue, Athena, Redshift, Bedrock, ECS Fargate, Lambda) Please apply online for consideration. More ❯