76 to 100 of 338 MLflow Jobs in the UK

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 first. More ❯

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 first. More ❯

building end-to-end scalable ML infrastructure with on-premise DGX or cloud platforms including AWS EKS/SageMaker, Azure Machine Learning/AKS, or common ML platforms (ClearML, MLflow, Weights and Biases). Cloud & Automation: Strong understanding of AWS, Azure, containerization/Kubernetes, multiple automation/DevOps, and ML lifecycle practices. Data Handling: Practical knowledge in data wrangling, handling More ❯

data 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 More ❯

Tech-savvy : Proficient in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX). Strong command of cloud- AI tooling (e.g., AWS SageMaker, GCP Vertex AI, AzureML/AzureAI, MLflow, Weights & Biases). Well-rounded Engineer : Comfortable working with version-controlled codebases, DevOps pipelines, containerisation/microservices (Docker/Kubernetes), and Infrastructure-as-Code (Terraform). Cursor AI is widely More ❯

engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., PyTorch, MLFlow, JAX) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Ability to scope and effectively deliver projects Strong problem More ❯

Who We Are 🙋 For years, artists have been 'shooting in the dark' when it comes to their marketing. There's a clear lack of robust, transparent and accessible marketing tools for musicians and artists to use to promote their music. More ❯

Who We Are 🙋 For years, artists have been 'shooting in the dark' when it comes to their marketing. There's a clear lack of robust, transparent and accessible marketing tools for musicians and artists to use to promote their music. More ❯

Who We Are For years, artists have been 'shooting in the dark' when it comes to their marketing. There's a clear lack of robust, transparent and accessible marketing tools for musicians and artists to use to promote their music. 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 … Programming: 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 More ❯

SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL - Expert knowledge of ML Ops frameworks in the following categories: a) experiment tracking and model metadata management (e.g. MLflow) b) orchestration of ML workflows (e.g. Metaflow) c) data and pipeline versioning (e.g. Data Version Control) d) model deployment, serving and monitoring (e.g. Kubeflow) - Expert knowledge of automated artefact deployment More ❯

SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL - Expert knowledge of ML Ops frameworks in the following categories: a) experiment tracking and model metadata management (e.g. MLflow) b) orchestration of ML workflows (e.g. Metaflow) c) data and pipeline versioning (e.g. Data Version Control) d) model deployment, serving and monitoring (e.g. Kubeflow) - Expert knowledge of automated artefact deployment More ❯

SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL - Expert knowledge of ML Ops frameworks in the following categories: a) experiment tracking and model metadata management (e.g. MLflow) b) orchestration of ML workflows (e.g. Metaflow) c) data and pipeline versioning (e.g. Data Version Control) d) model deployment, serving and monitoring (e.g. Kubeflow) - Expert knowledge of automated artefact deployment More ❯

engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Strong problem-solving skills and the ability to analyse issues More ❯

Strong understanding of SQL, NoSQL, and data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative mindset and ability to work in a fast-paced, small team More ❯

and SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning (e.g. Data Version Control) model deployment, serving and monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based More ❯

and SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning (e.g. Data Version Control) model deployment, serving and monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based More ❯

in 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 More ❯

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). #J-18808-Ljbffr More ❯

/or real-time systems Have knowledge of DevOps technologies such as Docker and Terraform, building APIs, CI/CD processes and tools, and MLOps practices and platforms like MLFlow and monitoring Have experience with agile delivery methodologies Have good communication skills Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline Nice to have Hands-on … technology stack Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, TensorFlow, etc...) PySpark AWS cloud infrastructure: EMR, ECS, S3, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow, Jenkins More Information Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, extra festive time off, and excellent family More ❯

learn). 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. More ❯

learn). 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. More ❯

Excellent communication skills and a passion for mentoring and team leadership. Nice to Have Experience with data engineering tools (Airflow, Kafka, Spark). Knowledge of MLOps practices and tools (MLflow, Kubeflow). Contributions to open-source projects or AI research. Why Join? Work on impactful AI products used by global clients. Competitive salary, stock options, and flexible working hours. A More ❯

with scalable deployment of data processing and machine learning models (batch as well as real-time). Practical experience in developing and maintaining ML systems with tools such as MLflow, BentoML, and Evidently AI. Exposure to learning methodologies leveraging advanced modeling frameworks such as PyTorch and TensorFlow will be beneficial. Familiarity with data governance and compliance standards. Certification as a More ❯

objectives. Experience using R and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel More ❯