1 to 25 of 26 Amazon SageMaker Jobs in the UK

months Contract, Inside IR35 Delivery of AWS-based data & analytics capabilities, built on foundational services such as EMR, Glue, MWAA, Dynamo, Kinesis, Kafka and Sagemaker AWS Cloud Data Platforms Architecture and AWS Cloud Security & Infrastructure Insurance or Regulated Financial Services Industries Solutioning within a modern data architecture context such more »

and Engineering Teams to drive innovation and excellence About you Ability to Design and implement MLOps pipelines on AWS Experience with MLOps Frameworks, mainly SageMaker and MLFlow Strong programming experience in production environments (Python) Strong infrastructure knowledge (Terraform) Experience with DevOps tools (Docker, Kubernetes) Experience with integrating with data more »

intelligence, machine learning, natural language processing, computer vision, or related field 3+ years experience writing production ready code Prior experience with AWS Cloud stack (SageMaker, EC2, Lambda, S3, RDS) Benefits Competitive salary Remote working Participation in our share option scheme Pension enrolment 25 days holiday more »

EC2, ECR, ECS, Glue, Lambda, S3, Cloud Formation, Cloudwatch etc. Have proven experience deploying models and ML pipelines at scale, including experience using AWS SageMaker and/or Google Cloud AI. Support and drive implementation of features around NLP and LLMs. Tackle challenges related to user recommendation/matching more »

day to day basis: Work together with the AI labs, Data, Infrastructure, and Platform teams to design and deploy ML-based services using AWS Sagemaker and other cloud based solutions Analyse current AWS account setup for data scientists & MLEs and improve it together with StSt MLE & DevOps experts Understand … Collaborate with the AI Labs teams to advance current ML workflows and software development standards Qualifications Industry experience in AWS ML services, including AWS Sagemaker Experience in DevOps, Cloud Operations, ML-engineering Experience in AWS administration and permission management Expertise with building enterprise scale ML & LLM solutions High proficiency more »

for everybody. Your responsibilities: Work together with the AI labs, Data, Infrastructure, and Platform teams to design and deploy ML-based services using AWS Sagemaker and other cloud based solutions. Analyse current AWS account setup for data scientists & MLEs and improve it together with StSt MLE & DevOps experts. Understand … Labs teams to advance current ML workflows and software development standards. Qualifications Your skills and qualifications Industry experience in AWS ML services, including AWS Sagemaker Experience in DevOps, Cloud Operations, ML-engineering, AWS administration and permission management. Expertise with building enterprise scale ML & LLM solutions, Docker, IaC (Terraform), CI more »

pipelines. This role is critical in ensuring the high performance and reliability of our AI-driven solutions. Responsibilities: Deploy LLMs using platforms like Huggingface, Sagemaker, or similar. Manage deployment on multiple GPUs, ensuring optimal performance. Implement scaling and load balancing to handle varying loads efficiently. Design and maintain data more »

different databases. Experience of architecting data solutions across hybrid (cloud, on-premise) data platforms. Knowledge of data science platforms such as Azure Machine Learning, Amazon SageMaker, Databricks, DataRobot, Snowflake etc., to enable the use of data and tie in with roadmap aspirations. Strong knowledge of data modelling, data more »

engineering practices sch as CI/CD. Experience deploying/monitoring machine learning models and following best practices. The Ability to develop pipelines using SageMaker, MLFlow or similar frameworks. Strong experience with data programming frameworks such as Apache Spark. Understanding of common Data Science and Machine Learning models, libraries more »

AI systems and craft innovative solutions! 🚀 Daily Tasks: 🔹 Collaborate with AI labs, Data, Infrastructure, and Platform teams to deploy ML-based services using AWS Sagemaker and other cloud solutions. 🔹 Analyze and improve current AWS setups for data scientists & MLEs. 🔹 Design infrastructure to facilitate experimentation & deployment of AI systems. 🔹 Advance more »

ML to drive innovation and solve complex problems. You will shape the design of the AI systems (classical ML & LLMs) on AWS and AWS Sagemaker and drive best practises when it comes to Infrastructure as code, CICD and other cloud native deployment approaches. Key Responsibilities: Develop and deploy scalable … degree or higher in Mathematics, Science, Computer Science, or a related field. Expertise building enterprise scale ML & LLM solutions. Proficiency with AWS services, including SageMaker, S3, and EC2. Strong programming skills in languages such as Python, Java, or Scala. Excellent problem-solving abilities and a proactive mindset towards tackling more »

Engineering Manager Type : Permanent Location : London Onsite Requirements : Hybrid - 2/3 days a week Salary : Ranging between £110,000 - £150,000 A leading global SaaS business at the forefront of cutting-edge technology are seeking a talented Engineer Manager more »

Previous role as an MLOps engineer Use of ML frameworks e.g. pytorch, tensorflow Familiarity with MLOps platforms e.g. Kubeflow, MLFlow, Tensorflow Extended, ClearML, VertexAI, Amazon SageMaker Experience deploying ML models in production environments Benefits We provide: Competitive salary, benchmarked against the market and reviewed annually Hybrid and/ more »

Key skills/knowledge/experience: Experience of AWS Data platform experience including AWS-EMR Cluster Platform, Glue, EC2, S3, VPC, IAM, RDS, Redshift, Sagemaker, Lake formation and Glue catalogue. Data Storage Fundamentals. Cloud specific patterns and technologies. AWS Service Selection, Container design. Designing DevSecOps pipelines. Experience in data more »

store, manage, query and visualize big datasets. Create data and metric visualization tools, web UI dashboards for model performance and data drift monitoring. AWS Sagemaker, Terraform About you (ideally): We hire based on personality, potential, and enthusiasm to make a difference, then we give you the tools and skills … Demonstrable professional experience in analysis and advanced modelling/machine learning techniques to solve business problems using large scale, complex data sets Technical Insight: Amazon SageMaker; Particularly SageMaker Pipelines, Endpoints, Batch Transformation/Inference, o Ideally SageMaker Studio and related technologies Amazon MLOps technologies; IAM more »

EC2, ECR, ECS, Glue, Lambda, S3, Cloud Formation, Cloudwatch etc. Have proven experience deploying models and ML pipelines at scale, including experience using AWS SageMaker and/or Google Cloud AI. Support and drive implementation of features around NLP and LLMs. Tackle challenges related to user recommendation/matching more »

in Git, Unix, and pytest. You're familiar with popular Python data mining and wrangling libraries, including pandas, numpy, sklearn, pytorch, langchain, llamaindex, bedrock, sagemaker, huggingface, mlflow, gradio, Flask, FastAPI, SQLAlchemy, psycopg2, boto3, gunicorn, pytest, and scrapy Experience with cloud services like Microsoft Azure (Data Factory, ML Studio, Synapse more »

is plus) Manage production-grade LLM application involving RAG evaluation and improvements (RAGAS, advanced retrieval techniques), monitoring and visibility tooling and scaling LLM applications (Amazon SageMaker) Stay Current with Emerging Technologies: Keep up-to-date with the latest developments in data engineering, machine learning, and AI technologies to more »

Bert, Roberta, GPT4, etc. and NLP Open-Source Libraries. Strong commercial experience with Python and NLP tooling, Previous commercial exposure to LLM, GPT4, AWS, SageMaker Excellent communication skills and ability to engage with teams and colleagues. Benefits: Competitive salary up to £85 000 Outstanding career progression opportunities. Flexible working more »

in Python Familiarity with machine learning frameworks (like PyTorch) and libraries (like Natural Language Toolkit) Experience with AWS (ideally with Bedrock and/or SageMaker) Experience with agile development (Scrum) is a plus Experience working on a remote team Fluent in written and spoken English Responsibilities Include Design and more »

on user behaviour, personalisation and more. Ideally develop the core-logic for a personalisation algorithm Work in their technical environment of AWS, Snowflake and Sagemaker YOUR SKILLS AND EXPERIENCE The successful Data Scientist will have the following: Strong Python, AWS, Snowflake and Sagemaker Experience working in a product … your interest please send your CV to Luke via the apply link on this page Desired Skills and Experience - Strong Python, AWS, Snowflake and Sagemaker - Experience working in a product-first environment for a user-focused brand in a sector such as Video gaming, gambling, eCommerce, tech, etc. - Excellent more »

At Amazon, we're working to be the most customer-centric company on earth. One customer experience that were constantly taking to new levels is how we fulfill and deliver customers orders. Our goal is to delight our customers by delivering their packages quickly and accurately, anywhere in the … world. To meet this goal, Amazon is continually striving to innovate and provide best-in-class service levels by pioneering new products and services in the last mile delivery space. Come join the team and help us work hard, have fun, and make history! The EU DSP Management team … new, step-change initiatives for the organization. Onboarding data to our data warehouse by building automated data pipelines utilizing eg Python in Lambda or Sagemaker to invoke API calls. Using statistical techniques and tools for strategic deep dives; generating actionable insights and effectively communicating recommendations to senior leadership. Inventing more »

leading a data science team and managing complex projects. Expertise in machine learning, statistics, data management, and relevant technologies (e.g., Python, R, SQL, AWS SageMaker, Apache Airflow, Dbt, AWS Kinesis). Strong communication skills with the ability to explain complex data concepts to a non-technical audience. Knowledge of more »

learning algorithms applied to solving business problems. Proficiency in Python and popular Data Science frameworks (e.g. pytorch, pandas, numpy etc) and MLOps platforms (e.g. Sagemaker) Solid understanding of metrics, benchmarking and evaluation methodology AI-powered user-facing products. Experience working in highly collaborative environments with complex technical dependencies. Proven more »

for everybody. Your responsibilities: Work together with the AI labs, Data, Infrastructure, and Platform teams to design and deploy ML-based services using AWS Sagemaker and other cloud based solutions. Analyse current AWS account setup for data scientists & MLEs and improve it together with StSt MLE & DevOps experts. Understand more »