1 to 25 of 29 Amazon SageMaker Jobs in the UK

including model hosting, request routing, continuous batching, and KV‐cache optimization Deploy, host, and lifecycle‐manage open‐source and proprietary LLMs on Amazon EKS and Amazon SageMaker, as well as on‐prem and local GPU clusters, using reproducible infrastructure as code and continuous delivery pipelines Implement observability … experience operating LLM inference servers such as vLLM and llm‐d (or directly equivalent serving stacks) Hands‐on experience hosting and serving LLMs on Amazon EKS and/or Amazon SageMaker, and on local GPU infrastructure Knowledge of LLM reliability and risk considerations, including latency/throughput ...

delivery of a production-grade machine learning platform on AWS. What you’ll be doing Design and maintain a production MLOps platform on Amazon SageMaker (Studio, Training, Pipelines, Endpoints) — including model registry, automated retraining, drift monitoring, and governance gates Lead the migration of a 12-model production suite … e.g., the CVM suite) from legacy infrastructure to SageMaker, owning parity testing methodology and sign-off Build and maintain CI/CD pipelines (CodePipeline/CodeBuild or equivalent) for automated model promotion across environments Define and enforce IAM least-privilege policies, KMS key management, and VPC/PrivateLink network ...

Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within: Microsoft Azure Google Cloud Platform (GCP) Additional experience with: Docker Kubernetes Git CI/CD Pipelines ...

serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic ...

architecture, data engineering, or enterprise infrastructure design, Cloud Mastery: Deep architectural expertise in at least one major cloud platform and its AI ecosystem: AWS: SageMaker, Bedrock, Glue, Redshift. Azure: Azure OpenAI Service, Azure Machine Learning, Synapse. GCP: Vertex AI, BigQuery ML, Dataflow. The ideal candidate will have strong experience ...

including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with containerisation, IaC, event ...

tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation, A/B testing and online evaluation ...

using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including version control, testing, and API/microservices architecture. Experience with MLOps platforms (e.g., MLflow, SageMaker) or agentic AI frameworks. Familiarity with secure development practices or regulated/high-compliance environments. Background in NLP, computer vision, or data engineering platforms. ...

agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing, observability and production support. Modern ML/ ...

Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling. Experience integrating human-in-the-loop or user feedback signals into ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands-on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML) Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

developing a growing team of Data Scientists Establishing best practice across model development, governance and MLOps Tech Stack Python | SQL | TensorFlow | PyTorch | AWS | Azure | SageMaker | NLP | LLMs | MLOps What we're looking for: Strong commercial Data Science and Machine Learning experience Experience leading teams or acting as a technical ...

business challengesMentoring and developing a growing team of Data ScientistsEstablishing best practice across model development, governance and MLOpsTech StackPython | SQL | TensorFlow | PyTorch | AWS | Azure | SageMaker | NLP | LLMs | MLOpsWhat we're looking for:Strong commercial Data Science and Machine Learning experienceExperience leading teams or acting as a technical lead within ...

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 ...

Tungsten Automation solutions Production AI Experience : Leverage experience with deploying, monitoring, and maintaining AI/LLM models in production (e.g., Azure ML, AWS Sagemaker, or Kubernetes-based ML pipelines) Thought Leadership : Provide thought leadership on AI-driven automation trends and best practices, both internally and externally Collaboration and Cross ...

with the Data Engineering and AI Platform teams, you will drive excellence and pace in the build, deployment, testing, and monitoring of models using Amazon Sagemaker [and occasionally Snowflake] Causal Inference & Decision Science: Lead the development of advanced causal models (e.g., Media Mix Modelling, retention drivers, and simulation ...

skills for querying structured datasets Proven experience developing and validating machine learning models Experience with ML deployment using containerisation or pipeline tools such as SageMaker or similar Strong experience with Git for version control Solid understanding of time series analysis and trend evaluation Excellent exploratory data analysis skills Desirable ...

environment evolves, but typical tools include: Python for experimentation, modelling, and evaluation Weights & Biases (or equivalent) for experiment tracking AWS , including services such as SageMaker and Bedrock Internally supported AI development platforms and tooling This role is not suited to candidates who are dogmatic about specific tools . Adaptability ...

experience delivering successful digital/AI products in complex, regulated environments. Familiarity with technologies such as RPA (UiPath, Automation Anywhere), machine learning platforms (AWS SageMaker, Azure ML, etc.), and natural language processing. Excellent communication, stakeholder management, and cross‐functional collaboration skills. Ability to bridge technical and non‐technical audiences ...

model monitoring, content safety, LLMOps, GenAIOps or production support for AI systems Experience with non‐Microsoft AI platforms or services, such as AWS Bedrock, Amazon Q, SageMaker, Google Vertex AI, Anthropic Claude, OpenAI APIs or open‐source model ecosystems Familiarity with ISO 42001, NIST ...

aware performance tuning. You will own reliability, performance, and cost efficiency of the LLM platform end-to-end, manage diverse hosting environments including EKS, SageMaker, and on‐prem GPU clusters, and lead incident response with strong #J-18808-Ljbffr ...

provide technical leadership across the full lifecycle of machine learning systems while architecting production-grade ML solutions. You will work with AWS SageMaker, lead teams on MLOps strategies, and ensure systems adhere to governance standards. This is a hands-on, technical position ideal for those with deep expertise ...

contract MUST have experience of ML in Snowflake. Looking for a Machine Learning Engineer to lead a migration of ML capabilities from existing AWS Sagemaker to Snowflake ML - will need to proof out can existing ML models work in Snowflake click apply for full job details ...

Azure, and on-premise environments, driving modernisation, scalability, security, and performance.You’ll play a key role in architecting advanced analytics platforms using AWS SageMaker, Azure Machine Learning, and SAS 9.4/Viya, including leading SAS Viya migration and transformation initiatives. Key Responsibilities Lead end-to-end solution architecture across … scalable, secure, and resilient analytics and AI platforms. Drive SAS 9.4 to SAS Viya migration and platform modernisation. Architect machine learning solutions using AWS SageMaker and Azure Machine Learning. Define data platform architecture, analytics pipelines, and enterprise integration patterns. Collaborate with engineering teams to implement CI/ ...