1 to 25 of 29 Remote Permanent Amazon SageMaker Jobs

ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda, SageMaker), Kubernetes, Terraform/CDK ML Ops & Automation: MLFlow, feature stores, model monitoring, A/B testing Data Security & Compliance: Secure SDLC, ISO/NIST ...

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 ...

experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs ...

experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs ...

vector databases (Pinecone, Weaviate, OpenSearch), or multi-agent orchestration. Experience building conversational agents or AI-driven automation systems. Knowledge of MLOps tooling such as SageMaker, MLflow, or Databricks. Familiarity with RLHF, alignment techniques, or model interpret ability. Interest in consumer behaviour, research methodologies, or product decision-making. Startup experience ...

vector databases (Pinecone, Weaviate, OpenSearch), or multi-agent orchestration. Experience building conversational agents or AI-driven automation systems. Knowledge of MLOps tooling such as SageMaker, MLflow, or Databricks. Familiarity with RLHF, alignment techniques, or model interpret ability. Interest in consumer behaviour, research methodologies, or product decision-making. Startup experience ...

best practices to build robust data pipelines and deploy models at scale. You'll use technologies like Python (and Clojure), AWS services (Athena, Bedrock, SageMaker, etc.), dbt, Prefect, and CI/CD automation with monitoring to ensure models are reliable and up-to-date. Collaborate across teams of data ...

Strong understanding of the ML lifecycle, including data pipelines, model training, evaluation, deployment, and monitoring Familiar with MLOps tools such as MLflow, Kubeflow, or SageMaker Deep understanding of LLM orchestration, prompt engineering, tool use, and memory architectures Familiar with various LLM inference engines such as vLLM or SGLang Experience ...

deployment, e.g.: GCP/AWS/Kubernetes, Jenkins, GCS/S3, etc. Familiarity with large-scale MLOps tools, e.g.: tf.serving/Torchserve, Kubeflow/Sagemaker Pipelines/Vertex AI Pipelines, etc. Understanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow. ...

Responsibilities: Design, Build, and Maintain Scalable ML Infrastructure: Lead the design and development of scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker for efficient model training and deployment. Collaborate with Product Teams: Work closely with product teams to develop MVPs for AI-driven features, ensuring quick ...

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 ...

Technical Knowledge: Strong understanding of modern AI/ML technologies and cloud-based solutions. Tools & Platforms: Familiarity with Snowflake, Databricks, Azure AI Foundry , AWS SageMaker, and other leading platforms. Data Science Expertise: Knowledge of data science fundamentals, solution lifecycles, and MLOps methodology. Generative AI Knowledge: Experience with LLMs, neural ...

Python, SQL, and core ML libraries (e.g., Pandas, NumPy, Scikit-Learn, XGBoost). Strong experience working with cloud-based ML platforms (AWS preferred, e.g., SageMaker). Familiarity with model management and deployment tooling such as Git, MLflow, or CI/CD frameworks. Ways of Working. Clear and concise communicator ...

Redis. Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana. Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink. Professionalism around collaborating in a remote working environment and well tested, reproducible work. Exceptional ...

structured and unstructured databases (SQL, MongoDB, DuckDB) Strong understanding of cloud infrastructure (AWS, Azure, or GCP), especially cloud storage, compute, and ML tools (e.g., SageMaker, Vertex AI, Azure ML) Experience with data lake/data warehouse technologies (e.g., S3 + Glue, BigQuery, Snowflake, Delta Lake) Knowledge of machine learning ...

visualisation languages and libraries (SQL, python and/or R, matplotlib/ggplot etc.) as well as supporting tools and platforms (e.g. git, Sagemaker, VS Code) A strong understanding of statistical concepts (hypothesis testing, sampling, probability distributions). Excellent communication and collaboration skills to effectively bridge the gap between ...

/ML concepts: supervised and unsupervised learning, GenAI, LLMs, embeddings, MLOps, vector search. Experience designing AI solutions using tools such as Azure ML, AWS SageMaker, Google Vertex AI, Databricks, LangChain, and Hugging Face. Ability to develop architecture artefacts (e.g., HLDs), technical narratives, effort estimates, and proposal content. Exceptional storytelling ...

/ML concepts: supervised and unsupervised learning, GenAI, LLMs, embeddings, MLOps, vector search. Experience designing AI solutions using tools such as Azure ML, AWS SageMaker, Google Vertex AI, Databricks, LangChain, and Hugging Face. Ability to develop architecture artefacts (e.g., HLDs), technical narratives, effort estimates, and proposal content. Exceptional storytelling ...

Model Architecture , including LLMs and SLMs—performance, training, suitability and deployment. Hands-on experience with at least two of the following: AWS Bedrock/SageMaker, Google Vertex AI, Azure ML, OpenAI Solid understanding of statistics, machine learning, data science, big data and data engineering principles. Knowledge of cloud security ...

scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical concepts clearly to both technical and non-technical stakeholders using appropriate language and methods. Profiling data and analysing ...

scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical concepts clearly to both technical and non-technical stakeholders using appropriate language and methods. Profiling data and analysing ...

scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical concepts clearly to both technical and non-technical stakeholders using appropriate language and methods. Profiling data and analysing ...

scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical concepts clearly to both technical and non-technical stakeholders using appropriate language and methods. Profiling data and analysing ...

large-scale analytics engines (e.g. Spark/PySpark) and scripting languages such as Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage formats or frameworks (e.g. Parquet, Delta Lake, Fabric). Experience with DevOps/DataOps tooling and practices ...

large-scale analytics engines (e.g. Spark/PySpark) and scripting languages such as Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage formats or frameworks (e.g. Parquet, Delta Lake, Fabric). Experience with DevOps/DataOps tooling and practices ...