1 to 25 of 29 Amazon SageMaker Jobs in the UK

ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end More ❯

TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow). Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++). Strong understanding of cloud ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML) and containerized deployments (Kubernetes, Docker). Hands-on experience with data and model pipelines (feature stores, registries, distributed training, inference scaling). Knowledge of More ❯

with cloud-native development GCP preferred. ·Hands-on experience with GCP Vertex AI model endpoints, pipelines, embeddings, vector search or equivalent cloud-native AI/ML platforms e.g. AWS SageMaker, Azure ML and agent orchestration frameworks e.g. LangChain, LangGraph. ·Solid understanding of MLOps CI/CD, IaC Terraform, experiment tracking, model registry, and monitoring. ·Proven experience deploying and operating More ❯

experience with cloud-native development (GCP preferred). Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks such as LangChain and LangGraph Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying More ❯

experience with cloud-native development (GCP preferred). Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks such as LangChain and LangGraph Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying More ❯

or internal clients within large organisations) through RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools More ❯

generation (RAG). Strong grasp of software engineering principles (SOLID, design patterns), dependency injection, and scalable system design. Hands on experience with ML tooling such as MLflow, Snowflake, Databricks, SageMaker or Azure Machine Learning. Hands on production experience deploying and monitoring AI/ML systems on cloud platforms such as AWS, GCP or Azure BONUS SKILLS/EXPERIENCE: Contributions More ❯

science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to More ❯

developing and deploying ML models in production environments. Strong Python skills, with experience in frameworks like PyTorch, TensorFlow, or Hugging Face. Confident working in AWS or similar cloud environments (SageMaker, Lambda, Docker, etc.). Experienced in (or eager to explore) areas such as forecasting, optimisation, reinforcement learning, generative AI, or computer vision. Solid engineering mindset, you know how to More ❯

of stakeholders* Confidence estimating development effort and costs Nice to have * CI/CD experience (Azure DevOps, GitHub Actions, Jenkins)* Exposure to ML Ops tooling (Azure ML, AI Foundry, SageMaker or Vertex AI)* Infrastructure-as-Code (Bicep, ARM, Terraform)* Previous work building Gen AI-powered products or automation Tech environment you'll be joining Python, C#Azure Data Factory, Logic More ❯

services. Hands on experience with DevOps and engineering tools (GitLab, GitHub, Azure DevOps, Docker, Kubernetes). Proficiency with AI/ML and MLOps platforms (Databricks, Google Cloud Vertex AI, SageMaker). Familiarity with generative AI technologies and frameworks (OpenAI, Google Gemini, Hugging Face Transformers). Demonstrated success in developing and executing product strategies. Ability to lead and inspire cross More ❯

model development, validation, deployment, monitoring, and maintenance Awesome If You: Have experience in the energy sector or understanding of energy systems and operations Have practical experience with AWS services (SageMaker, S3, EC2, Lambda, etc.) Have experience with infrastructure as code tools (Terraform, CloudFormation) Have experience with Apache Kafka and real-time streaming frameworks Are familiar with observability principles such More ❯

version control, CI/CD, and collaboration. • Strong coding background in Python and PySpark. • Hands-on experience with the following AWS services: o S3, Lambda, Glue, Step Functions, Athena, SageMaker, VPC, ECS, IAM, KMS • Proficiency in CloudFormation for infrastructure automation. • Experience with unit testing frameworks and best practices. • Familiarity with GitLab for source control and CI/CD ABOUT More ❯

.NET/C# (Key to have a background in C#) Knowledge of AI principles and AI ethics Knowledge of data safety in LLM usage Experience with: AWS: boto3, Bedrock, SageMaker, Lambda, S3, EC2 Azure: Azure OpenAI Service, Cosmos DB Retrieval-Augmented Generation (RAG), Graph RAG Embedding models and LLM training fundamentals Damia Group Limited acts as an employment agency More ❯

ahead of the curve in Generative AI, ML infrastructure, and cloud-native tooling Tech Stack Programming: Python (Java familiarity is a plus) AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more On-Prem: Managed Kubernetes Platform and Hadoop ecosystem Why This Role is Different Direct Impact: Build AI tools that traders and quants use daily to optimize strategies More ❯

required to contribute: Years of overall Data and Analytics experience with 2. Minimum 10+ years in AWS data platform including AWS S3, AWS Glue, AWS Redshift, AWS Athena, AWS Sagemaker, AWS Quicksight and AWS MLOPS 3. Snowflake DWH architecture, Snowflake Data Sharing, Snowpipe, Polaris catalog and data governance (meta data/business catalogs). 4. Knowledge of at least More ❯

AI solutions are ethical, scalable, and reliable in production. Your skillset Proven experience designing and scaling enterprise-grade AI/ML systems. Expertise with cloud AI stacks (AWS Bedrock, SageMaker, Azure OpenAI, Azure ML). Strong skills in Python, FastAPI, CI/CD, MLOps, and LLM orchestration. Leadership experience across multidisciplinary AI teams. Strategic mindset — able to bridge deep More ❯

A team-led hybrid working arrangement is in place. About you To be successful in this role it’s essential you have: Extensive experience with AWS (S3, Glue, Redshift, SageMaker) Experience of building and automating data pipelines in finance The ability to demonstrate, automate and manage data systems so they run smoothly and can grow easily. Familiarity with Docker More ❯

APIs, and services used globally. Develop in one or more core technology areas: NoSQL (MongoDB, DocumentDB) Graph databases (GraphDB, Neo4j) Search technologies (ElasticSearch, OpenSearch, SOLR) ML/AI frameworks (SageMaker, Bedrock) Cloud-based data engineering (AWS) Build and maintain cloud-native data pipelines, ensuring data quality, accuracy, and reliability. What We're Looking For 5+ years' experience in software More ❯

Claude, Gemini, Mistral, LLaMA, Cohere). Experiment with advanced prompting techniques; Chain-of-Thought, ReAct, Tree-of-Thoughts, and more. Deploy AI/ML pipelines using Azure ML, AWS SageMaker, Vertex AI, or Databricks. Integrate LLMs into production apps using LangChain, LlamaIndex, and RAG architectures. Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like … related field. Tech Stack LLMs: GPT-4/5, Claude, Gemini, Mistral, LLaMA, Cohere Frameworks: LangChain, LlamaIndex, Haystack Tools: GitHub Copilot, Cursor, PromptLayer, Weights & Biases Cloud: Azure ML, AWS SageMaker, Google Vertex AI, Databricks Infra: Python, Docker, Kubernetes, SQL, PyTorch More ❯

Senior Machine Learning Engineer page is loaded Senior Machine Learning Engineer Apply locations London, UK time type Full time posted on Posted 5 Days Ago job requisition id R15074 Job Title Senior Machine Learning Engineer Job Description Here at UnderwriteMe More ❯

search, reranking, embedding optimization, chunking strategies and evaluation harnesses. Develop APIs, microservices and serverless workflows for scalable deployment. ML/LLM Engineering Work with AI+ML pipelines throughAzure ML,AWS SageMaker,Vertex AI,Databricks, orModal/Fly.iofor lightweight LLM deployment. Utilizevector databases(Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores. UseAI-powered dev tools(GitHub Copilot, Cursor, Codeium, Aider, Windsurf … 2.x, Mistral Large, LLaMA 3, Cohere Command R+, DeepSeek Frameworks: LangChain, LlamaIndex, Haystack, AutoGen, CrewAI Tools: GitHub Copilot, Cursor, LangSmith, LangFuse, Weights & Biases, MLflow, Humanloop Cloud: Azure ML, AWS SageMaker, Google Vertex AI, Databricks, Modal Infra: Python, Docker, Kubernetes, SQL/NoSQL, PyTorch, FastAPI, Redis More ❯

from strategic planning through to pre-production deployment and optimisation Architect and implement advanced solutions leveraging AWS's AI/ML services, with particular focus on Generative AI using Amazon Bedrock and SageMaker Provide technical leadership and mentorship to junior consultants while driving best practices across delivery teams Partner with customers to translate business challenges into measurable ML … candidates to apply. If 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's why customers from the most … software development life cycle (sdlc) and agile/iterative methodologies Experience in using Python and hands on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We More ❯

Data ScientistHybrid Working - Local Site - 1-2 days a week on site.Financial ServicesLorien's leading banking client is looking for a number of Data Scientists to join them on a new long term project which will be working on GenAI More ❯

Support proposal writing, solution direction, pricing, and costing. Define and implement data governance, security, and compliance strategies. Work hands-on with AWS services such as Redshift, Glue, Lake Formation, SageMaker, Athena, and more. Contribute to pre-sales activities and client bid responses. Mentor junior team members and contribute to internal capability building. Your Skills and Experience Required Skills & Experience … Proven experience in AWS cloud architecture, particularly in data and analytics. Strong hands-on expertise with AWS services (e.g. Redshift, Glue, Lake Formation, SageMaker). Experience designing scalable data platforms, including data lakes, lakehouses, and real-time analytics. Demonstrated success in data modernisation projects, including migration from on-premise to cloud-native platforms. Knowledge of automation tooling (e.g., CI More ❯