1 to 25 of 36 Amazon SageMaker Jobs

for accessing and processing data (PostgreSQL preferred but general SQL knowledge more important) Experience using the latest Data Science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g. Tensorflow, MXNet, scikit-learn) Delivering insights using visualisation tools or libraries (Javascript preferred) Experience building and deploying solutions to Cloud (AWS more »

Good grasp of architecture and how best to integrate machine learning into backend services. Some experience with MLOps processes and tools. Experience with AWS SageMaker would be ideal. Understanding of CI/CD & DevOps principles. Experience working with cloud environments. If you feel you meet most, but not all 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 »

Kubernetes, 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, suspicious usage 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 »

Kubernetes, 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, suspicious usage 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 »

models to build a better future where a new job is just a click away. Your responsibilities: Implement and productionise ML-based services using Sagemaker Collaborate with Data Scientists to design and implement LLM-based inference workflows Utilize IaC and CI/CD best practices to increase efficiency in … years of experience in Machine Learning Engineering, Data Engineering, or a related field Proficiency in production-level Python (Clean, SOLID) and industry experience with Amazon Sagemaker deployment of LLMs Experience with LLM-related packages, such as PyTorch, Transformers etc. Experience with IaC (Terraform), CI/CD, and test 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 »

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 »

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 »

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 »

deploying industrial grade AI solutions •Experience in using AI frameworks, trending models and libraries, such as TensorFlow, PyTorch, GPT, Gemini, LaMDA, LangChain, Vertex AI, SageMaker, etc •Experience in using cloud services and platforms, such as GCP, AWS, etc •Enthusiasm, strong analytical mind, willingness to learn and desire to make 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 »

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 »

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 »

and HTML as well as audio and video files Apps based on LLMs and including RAG and deploying to public cloud, including Azure & AWS. Amazon SageMaker to monitor and scale LLM apps. Key Skills: degree in Computer Science, AI, ML, or a related field (desired). experience in more »

/Python-based REST APIs, and API specifications using Swagger/OpenAP Functional programming and object-orientated programming AWS services (including Kinesis, ECR, Lambdas, SageMaker and DynamoDb) or other cloud services Sound understanding of risk and risk mitigation design in relevant areas, including (but not limited to) fraud, financial more »

or related field, or equivalent experience.Experience creating LLM & RAG driven applicationsProficiency in programming with tools like Python, TensorFlow, and PyTorch.ML deployment with services like Sagemaker, AzureML, VertexAI etc.A keen understanding of AI ethics and responsible AI deployment.Innovative thinking and excellent problem-solving abilities.Desirable skills:Experience with cloud infrastructures, especially more »

or equivalent experience. Experience creating LLM & RAG driven applications. Proficiency in programming with tools like Python, TensorFlow, and PyTorch. ML Deployment with services like Sagemaker, AzureML, VertexAI etc. A keen understanding of AI ethics and responsible AI deployment. Innovative thinking and excellent problem-solving abilities. Desirable skills: Experience with more »

data 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 pipelines for model 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 »

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 »

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 »