1 to 25 of 35 Permanent Amazon SageMaker Jobs in the UK excluding London

3+ years of experience in machine learning operations, data engineering, or related roles AWS Proficiency: Strong understanding of AWS services (e.g., EC2, S3, Lambda, SageMaker, ECS) and cloud infrastructure management Programming and ML Frameworks: Proficiency in Python and experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch 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 ❯

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 ❯

Collaborate with ML/AI Teams Package and deploy large‑language‑model (LLM) training jobs on distributed GPU clusters (Slurm, Ray, Kubeflow, or AWS SageMaker). Optimize model‑serving (Triton, vLLM, TorchServe) for low‑latency, high‑throughput inference. Cost & Performance Optimization Track cloud spend, right‑size resources, and introduce 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 in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. More ❯

experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. More ❯

Serve models using vLLM, DeepSpeed, Triton, TensorRT Multi-Agent Orchestration: Work with LangChain, AutoGen, CrewAI, DSPy and similar tools Cloud & MLOps (AWS): Deploy with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing More ❯

experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. More ❯

right approach Previous experience developing real-world ML products in areas like demand forecasting, optimisation, or computer vision Comfortable working with AWS services (e.g. SageMaker, Lambda) Strong grasp of statistics and modelling, with the ability to turn raw data into actionable insight Willingness to learn the basics of backend 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 ❯

working with large data sets with a mathematical and analytical mindset, they are looking for senior engineers to join the team working with AWS SageMaker for AI and ML as well as research and cleansing of data sets. More ❯

Cloud Functions, Google BigQuery and Looker Create Solution architectures for Data Engineering and Analytics projects using AWS products like S3, Redshift, QuickSight and AWS SageMaker Build High performance scalable architecture presentations and generate SOW for customer Data Engineering and Analytics Projects Involve yourself in Pre-sales of Data Engineering More ❯

through research Job Qualifications We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices More ❯

Knowledge of Machine Learning and NLP unstructured data. Implemented ML platform in enterprise client experience of ML use-cases AIML Platforms: DataRobot, Dataiku, Anaconda, Sagemaker, Vertex etc. AIML Frameworks: MLFlow, KubeFlow, BentoML, TensorFlow etc. AIML Development: PyTorch, Jupyter Notebooks, XGBoost, Tensorflow etc. AIML Models: Linear/Logistic Regression, KNN More ❯