1 to 25 of 37 Permanent Amazon SageMaker Jobs

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 »

Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and deployment. Data Analytics and Big Data Technologies: Proficient in big data technologies such as Hadoop, Spark more »

Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and deployment. Data Analytics and Big Data Technologies: Proficient in big data technologies such as Hadoop, Spark more »

Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and deployment. Data Analytics and Big Data Technologies: Proficient in big data technologies such as Hadoop, Spark more »

Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and deployment. Data Analytics and Big Data Technologies: Proficient in big data technologies such as Hadoop, Spark more »

Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and deployment. Data Analytics and Big Data Technologies: Proficient in big data technologies such as Hadoop, Spark more »

Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and deployment. Data Analytics and Big Data Technologies: Proficient in big data technologies such as Hadoop, Spark more »

across one or more CSPs or third-party vendor technologies, including Google PaLM and Vertex AI, Azure OpenAI Services and AzureML, AWS Bedrock and Sagemaker, Dataiku, Databricks, Snowflake SnowPark and Snowflake Cortex, Data Robot.Experience in architecting scalable, performant & cost optimized AI/ML solutions leveraging; serverless technologies, container/ more »

across one or more CSPs or third-party vendor technologies, including Google PaLM and Vertex AI, Azure OpenAI Services and AzureML, AWS Bedrock and Sagemaker, Dataiku, Databricks, Snowflake SnowPark and Snowflake Cortex, Data Robot.Experience in architecting scalable, performant & cost optimized AI/ML solutions leveraging; serverless technologies, container/ more »

across one or more CSPs or third-party vendor technologies, including Google PaLM and Vertex AI, Azure OpenAI Services and AzureML, AWS Bedrock and Sagemaker, Dataiku, Databricks, Snowflake SnowPark and Snowflake Cortex, Data Robot.Experience in architecting scalable, performant & cost optimized AI/ML solutions leveraging; serverless technologies, container/ 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 »

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 »

Python and its libraries for data manipulation, analysis, and machine learning (e.g., scikit-learn, TensorFlow, PyTorch). Strong knowledge of AWS services, particularly AWS Sagemaker, and experience working within an AWS environment. Experience with large language models (LLMs) and their applications is highly desirable. Solid understanding of software engineering 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 »

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 »

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 »

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 »

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 »

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 »

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 »

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 »

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 »