15 of 15 Amazon SageMaker Jobs in the UK

infrastructure Comfortable working in at least one major cloud provider. AWS, GCP or Azure Familiar with Docker and CI/CD pipelines Experience with managed ML platforms such as SageMaker, Vertex AI or Azure ML Data engineering and MLOps Experience with data warehouses such as Snowflake, BigQuery or Redshift Workflow orchestration using tools like Airflow or Dagster Experience with More ❯

geospatial libraries such as GeoPandas, Shapely, and GDAL Advanced knowledge of PostgreSQL/PostGIS for spatial data management Experience with AWS and Azure platforms, including AI services (e.g., AWS SageMaker, Azure ML) Proven experience developing or deploying AI models across domains such as natural language processing, computer vision, or predictive analytics Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch 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 ❯

using SQL and NoSQL databases. Hands-on ownership of CI/CD (Jenkins/Azure DevOps/GitHub Actions) and MLOps in Azure ML/Azure AI Foundry/SageMaker/Vertex AI. Working knowledge of Infrastructure as Code (Bicep, ARM, Terraform). A test-driven development mindset and commitment to engineering quality. Broad understanding of ML approaches, with More ❯

scientist to build and deploy machine learning models. Hands-on Skills: Data Engineering Coding background and Hands on experience on below AWS services. S3, Lambda, Glue, Step Function, Athena, Sagemaker, VPC, ECS, IAM, KMS etc. CloudFormation Python Unit Testing Gitlab PySpark AI/ML knowledge (Good to have More ❯

scientist to build and deploy machine learning models. Hands-on Skills: Data Engineering Coding background and Hands on experience on below AWS services. S3, Lambda, Glue, Step Function, Athena, Sagemaker, VPC, ECS, IAM, KMS etc. CloudFormation Python Unit Testing Gitlab PySpark AI/ML knowledge (Good to have More ❯

scientist to build and deploy machine learning models. Hands-on Skills: Data Engineering Coding background and Hands on experience on below AWS services. S3, Lambda, Glue, Step Function, Athena, Sagemaker, VPC, ECS, IAM, KMS etc. CloudFormation Python Unit Testing Gitlab PySpark AI/ML knowledge (Good to have 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 ❯

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 ❯

from architecture to production—and help shape best practices in security, automation, and cost-efficient AI deployment. Key Responsibilities Architect secure, scalable ML and GenAI systems using AWS services (SageMaker, EKS/Fargate, Lambda, Step Functions). Build CI/CD pipelines for ML models, agents, and GenAI applications. Deploy LLMs and orchestrate multi-agent systems (LangChain, LangGraph, or … across all ML workflows. Mentor junior engineers and promote engineering best practices. Key Requirements Essential 3+ years experience in MLOps/ML Engineering/AIOps. Strong expertise in AWS (SageMaker, EKS/Fargate, Lambda, Step Functions, S3). Proficient with Docker, Kubernetes , and IaC (Terraform or AWS CDK). Experience with MLflow , CI/CD tools, and automated testing. More ❯

maintain the data platform Building infrastructure and data architectures in Cloud Formation, and SAM. Designing and implementing data processing environments and integrations using AWS PaaS such as Glue, EMR, Sagemaker, Redshift, Aurora and Snowflake Building data processing and analytics pipelines as code, using Python, SQL, PySpark, spark, CloudFormation, lambda, step functions, Apache Airflow Monitoring and reporting on the data … 6+ years of experience in a Data Engineering role. Strong experience and knowledge of data architectures implemented in AWS using native AWS services such as S3, DataZone, Glue, EMR, Sagemaker, Aurora and Redshift. Experience administrating databases and data platforms Good coding discipline in terms of style, structure, versioning, documentation and unit tests Strong proficiency in Cloud Formation, Python and 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 ❯

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 ❯

bid responses. Implement Governance and Security: Define and enforce data governance, security, and compliance strategies. Hands-On Expertise: Work with AWS services such as Redshift, Glue, Lake Formation, Athena, SageMaker, and more. Mentor and Build Capability: Support the growth of junior team members and contribute to internal capability development. What We're Looking For AWS Expertise: Proven experience designing … and implementing data platforms using AWS services (Redshift, Glue, Lake Formation, SageMaker, Athena). Data Architecture Skills: Strong background in data lakes, lakehouses, data warehouses, and real-time analytics solutions. Cloud Modernisation Experience: Experience migrating on-premises systems to cloud-native architectures. Automation & DevOps: Familiarity with CI/CD and AWS DevOps practices. Containerisation & Orchestration: Knowledge of Docker, Kubernetes More ❯

their consulting delivery team, supporting key financial services and banking clients. The assignment focuses on designing and building an advanced model developer environment for containerised model deployments on AWS SageMaker , with GenAI/agentic capabilities embedded into the platform. You will also lead a team of four developers and collaborate closely with data engineering, data science and platform teams. … Experience Banking experience is essential Strong software engineering background with expert Python Hands-on experience with GitLab CI/CD Extensive Docker containerisation & runtime experience Strong understanding of AWS SageMaker deployments Experience building developer environments for ML/quant/data-intensive workflows Knowledge of monitoring, logging, reporting and test automation Proven team leadership of small development teams Strong More ❯