1 to 25 of 101 Kubeflow Jobs in London

GPT, BERT, T5 family). Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. Open-source contributions or published papers in top-tier AI/ML conferences (NeurIPS, ICML, CVPR, ACL, etc.). ⸻ Soft Skills & Cultural More ❯

in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). What we offer Culture of caring: At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people More ❯

in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). What we offer Culture of caring: At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people More ❯

in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). What We Offer Culture of Caring: At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people More ❯

Machine Learning Engineer with Data Engineering expertise Machine Learning Engineer with Data Engineering expertise 2 weeks ago Be among the first 25 applicants Tadaweb is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices More ❯

challenges Preferred Qualifications Experience with real-time data processing (Kafka, Kinesis, Flink) Knowledge of containerization and infrastructure-as-code (Docker, Kubernetes, Terraform) Familiarity with MLOps practices and tools (MLflow, Kubeflow, etc.) Experience with data governance frameworks and data cataloging Understanding of graph databases and unstructured data processing Knowledge of advanced analytics techniques and statistical methods Experience with data mesh or More ❯

work with clients and cross-functional teams. Preferred to Have: PhD in Computer Science, AI/ML, or related field. Experience with production ML systems and MLOps pipelines using Kubeflow or Vertex AI Pipelines. Knowledge of transformers and large language models (LLMs). Understanding of recommender systems, natural language processing, or graph-based search engines. Contributions to open-source ML More ❯

Address computer vision, NLP, and generative tasks using PyTorch, TensorFlow, or Transformer-based models. Model Deployment & MLOps Integrate CI/CD pipelines for ML models using platforms like MLflow, Kubeflow, or SageMaker Pipelines. Monitor model performance over time and manage retraining to mitigate drift. Business Insights & Decision Support Communicate analytical findings to key stakeholders in clear, actionable terms. Provide data … Python (NumPy, Pandas), R, SQL. ML/DL Frameworks: Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers. Big Data & Cloud: Databricks, Azure ML, AWS SageMaker, GCP Vertex AI. Automation: MLflow, Kubeflow, Weights & Biases for experiment tracking and deployment. Architectural Competencies Awareness of data pipelines, infrastructure scaling, and cloud-native AI architectures. Alignment of ML solutions with overall data governance and security More ❯

DevOps, cloud infrastructure, or site reliability engineering Strong expertise in multi-cloud and hybrid infrastructure including AWS, GCP, and on-premises environments Experience with MLOps tooling such as MLFlow, Kubeflow, DataRobot, or similar platforms for ML lifecycle management Experience with containerization and orchestration (Docker, Kubernetes) specifically for ML workloads and GPU clusters Deep understanding of CI/CD pipelines for More ❯

Tadaweb is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices in the United Kingdom, France, and the United States. For over 13 years, we’ve been on a mission to make the world More ❯

Tadaweb is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices in the United Kingdom, France, and the United States. For over 13 years, we’ve been on a mission to make the world More ❯

Tadaweb is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices in the United Kingdom, France, and the United States. For over 13 years, we’ve been on a mission to make the world More ❯

Tadaweb is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices in the United Kingdom, France, and the United States. For over 13 years, we’ve been on a mission to make the world More ❯

. Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines, version control, and ML workflows (MLflow, Kubeflow). Proven track record of delivering ML models that solve real-world business challenges at scale. Excellent communication skills with the ability to work effectively in cross-functional teams. If More ❯

. Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines, version control, and ML workflows (MLflow, Kubeflow). Proven track record of delivering ML models that solve real-world business challenges at scale. Excellent communication skills with the ability to work effectively in cross-functional teams. If More ❯

to work with clients and cross-functional teams. Preferred Qualifications PhD in Computer Science, AI/ML, or related field. Experience with production ML systems and MLOps pipelines using Kubeflow or Vertex AI Pipelines. Knowledge of transformers and large language models (LLMs). Understanding of recommender systems, natural language processing, or graph-based search engines. Contributions to open-source ML More ❯

like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems More ❯

like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems More ❯

like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems More ❯

like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems More ❯

like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems More ❯

engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured approach More ❯

engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured approach More ❯

engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured approach More ❯

engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured approach More ❯