17 of 17 Remote Kubeflow Jobs

algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end AI/ More ❯

solutions (Snowflake, BigQuery, Redshift) Experience with cloud platforms (AWS, Azure, GCP) and their ML and AI services (SageMaker, Azure ML, Vertex AI) Knowledge of MLOps tools including Docker, MLflow, Kubeflow, or similar platforms Experience with version control (Git) and collaborative development practices Excellent analytical thinking and problem-solving abilities Strong communication skills with ability to explain technical concepts to business More ❯

years experience in AI/ML roles or relevant hands-on projects Nice to Have (Bonus) : NLP, Computer Vision, or Reinforcement Learning experience Knowledge of MLOps tools (MLflow, Kubeflow, etc.) Familiarity with SQL or Big Data tools (e.g., Spark) ️ Please apply only if you meet the skill and experience criteria. This role is open and hiring now don't delay. More ❯

LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms: AWS SageMaker, Azure ML, Google Vertex AI APIs & Orchestration: OpenAI API, Anthropic Claude, Weaviate, FastAPI (for AI applications) MLOps & Experimentation: Weights & Biases, DVC (Data Version More ❯

You will have relevant academic (research Masters level) and/or industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience More ❯

scalable training pipelines for large datasets. Working experience leading complex, cross-functional projects and influencing technical direction across multiple teams. Familiarity with modern workflow orchestration tools such as Prefect, Kubeflow, Argo, etc. Software engineering fundamentals, including data structures, design patterns, version control (Git), CI/CD, testing, and monitoring. Exceptional problem-solving skills, with a proven ability to navigate ambiguity More ❯

platform engineering teams to identify technical gaps, scope backend development efforts, and deliver core platform capabilities such as: Model training orchestration. CI/CD for ML (e.g., using MLflow, Kubeflow, or Vertex AI Pipelines). Model versioning, monitoring, and governance. Enable high-impact AdTech use cases including: Marketing Mix Modelling (MMM). Real-time personalisation and bidding. Audience segmentation and More ❯

with version controls systems (e.g. Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to More ❯

with version controls systems (e.g. Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to More ❯

with version controls systems (e.g. Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to More ❯

and new tools Required Skills and Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks More ❯

You will have relevant academic (research Masters level) and/or industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience More ❯

and on-prem hardware. They use both on-prem, self-managed systems and also leverage AWS infrastructure. Essential: Experience of managing an on-prem Kubernetes clusters. Working knowledge of Kubeflow and other systems such as MLflow Exposure to cloud based systems e.g. AWS, Azure or GCP (they use AWS) Knowledge of Linux systems including scripting and system configuration. Understanding of More ❯

city. There is no on-call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into … various technologies Contribute to the technical strategy and suggest improvements to the ML Ops stack Required Skills and Experience: Solid experience managing on-premise Kubernetes clusters Strong knowledge of Kubeflow or similar ML Ops platforms Proficiency in Python programming, Linux systems, and scripting Experience with AWS services (Cognito, S3, EC2, Lambda, etc.) Familiarity with ML frameworks such as PyTorch or More ❯

city. There is no on-call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into … various technologies Contribute to the technical strategy and suggest improvements to the ML Ops stack Required Skills and Experience: Solid experience managing on-premise Kubernetes clusters Strong knowledge of Kubeflow or similar ML Ops platforms Proficiency in Python programming, Linux systems, and scripting Experience with AWS services (Cognito, S3, EC2, Lambda, etc.) Familiarity with ML frameworks such as PyTorch or More ❯

per year Requirements: Minimum 1 to 2 years of proven experience in ML-Ops, including end-to-end machine learning lifecycle management Familiarity with MLOps tools like MLFlow, Airflow, Kubeflow or custom implemented solutions. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Github actions, Bitbucket Pipelines) Proficiency in … operations to improve efficiency Implement and manage in-cloud ML-Ops solutions, leveraging Terraform for infrastructure as code Technologies: Airflow AWS BitBucket CI/CD Cloud Embedded GitHub Support Kubeflow Machine Learning Mobile Python Terraform C++ DevOps More: Cinemo is a global provider of highly innovative infotainment products that make every screen an opportunity. Its range of award-winning, fully More ❯

scalable ML/AI systems in production environments • Build and deploy robust machine learning solutions using Python • Architect and manage Kubernetes-based ML infrastructure • Leverage MLOps frameworks (e.g., KServe, Kubeflow, MLflow) for end-to-end model lifecycle management • Contribute to and lead open source initiatives with visible community impact • Mentor junior engineers and guide technical direction • Implement and maintain CI … years in ML/AI systems in production • Strong Python development skills • Deep knowledge of Kubernetes and ML workload orchestration • Proven use of MLOps tools such as KServe, Kubeflow, or MLflow • Demonstrated leadership in open source communities • Experience with distributed system architecture • Solid foundation in engineering best practices Location Remote role working European hours . Applications are welcome from professionals More ❯