8 of 8 Remote Kubeflow Jobs

GCP) Strong understanding of MLOps (model monitoring, CI/CD for ML, versioning and deployment), with experience in MLOps frameworks such as MLFlow, ZenML, Kubeflow, Vertex AI and Sagemaker Experience mentoring and coaching junior engineers to enhance team capability Experience with data visualisation and analytics tooling (e.g., Thoughtspot, Streamlit, Metabase More ❯

and training across GPUs and cloud-based architectures. Ensure security and compliance for ML platforms handling sensitive data. Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency. Implement monitoring and alerting systems to detect anomalies and maintain model reliability. What We’re Looking For 3+ years of experience More ❯

and training across GPUs and cloud-based architectures. Ensure security and compliance for ML platforms handling sensitive data. Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency. Implement monitoring and alerting systems to detect anomalies and maintain model reliability. What We’re Looking For 3+ years of experience More ❯

Understanding of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI/CD pipelines in AI (e.g., MLflow, Kubeflow, SageMaker). AI for Systems Engineering Experience working with SysML, MBSE tools, or digital engineering pipelines. Understanding of how to map or extract system design More ❯

Kernel, and tools such as MS tooling, Co-Pilot Studio, ML Studio, Prompt flow, Kedro, etc. Proficiency with pipeline orchestration tools, such as Airflow, Kubeflow, and Argo. Outstanding communication skills, combining subject matter expertise with a flair for statistics. A results-driven attitude, a passion for innovation, and a self More ❯

cloud solutions such as Openstack, MicroCloud and Ceph, and solutions that could be deployed either on-premises or in public clouds such as Kubernetes, Kubeflow, Spark, PostgreSQL, etc. The team works hands-on with the technologies by deploying, testing and handing over the solution to our support or managed services More ❯

role in any domain Experience developing and deploying ML applications/solutions Experience with at least two MLOPs and Data Engineering tools (e.g., MLFlow, Kubeflow, Neptune, Airflow, DVC) Software development skills in at least two different programming languages (e.g. Python, R, C/C++), including familiarity with machine learning and … NoSQL, Cypher and other big data querying languages Experience with big data frameworks (Hadoop, Spark, Flink etc.) Experience with ML lifecycle management tools (MLflow, Kubeflow, etc.) Familiarity with data pipelining and streaming technologies (Apache Kafka, Apache Nifi, etc.) Demonstrated contributions to open-source software repositories (github, kaggle, etc.) Experience deploying More ❯

data pipelines across cloud/on-premises, using Azure and other technologies. Experienced in orchestrating data workflows and Kubernetes clusters on AKS using Airflow, Kubeflow, Argo, Dagster or similar. Skilled with data ingestion tools like Airbyte, Fivetran, etc. for diverse data sources. Expert in large-scale data processing with Spark … or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using Bash, PowerShell, or More ❯