1 to 25 of 44 MLflow Jobs in the UK excluding London

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 ❯

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 ❯

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 ❯

prompt engineering (e.g., 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.). More ❯

and TensorRT Proficiency in Python and ML/engineering frameworks such as PyTorch, TensorFlow (including Keras), Hugging Face (Transformers, Datasets) and scikit-learn, etc Experience with MLOps tools, including MLFlow, workflow orchestrators (Airflow, Metaflow, Perfect or similar), and containerisation (Docker) Strong knowledge of cloud platforms like Azure, AWS or GCP for deploying and managing ML models Familiarity with data engineering 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 ❯

of data governance, compliance, and security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE ARE your future. More details More ❯

of data governance, compliance, and security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE ARE your future. Full time More ❯

be beneficial but not essential: Data modelling approaches (Kimball, Imnon) Orchestration tools - Apache Airflow, Prefect or cloud-native tools Backend software development (Java, APIs, Scalability, Logging and Monitoring etc.) MLFlow and other MLOps/Machine Learning Engineering processes to support advanced analytical use cases LLMs and Agentic AI BI tools such as Tableau/Power BI Other desirable skills and More ❯

as TensorFlow, PyTorch, Scikit-learn, and Keras. Understanding of algorithms and techniques for supervised and unsupervised learning. Experience with tools for model monitoring, logging, and performance evaluation, such as MLflow or Prometheus. Strong scripting skills in Bash, PowerShell, or similar scripting languages for automation of tasks and ability to write reusable and maintainable code to streamline ML operations Proficient in More ❯

as TensorFlow, PyTorch, Scikit-learn, and Keras. Understanding of algorithms and techniques for supervised and unsupervised learning. Experience with tools for model monitoring, logging, and performance evaluation, such as MLflow or Prometheus. Strong scripting skills in Bash, PowerShell, or similar scripting languages for automation of tasks and ability to write reusable and maintainable code to streamline ML operations Proficient in More ❯

Node.js, Go). Experienced in deploying scalable AI models using cloud infrastructure (GCP preferred). Knowledgeable in containerisation (Docker) and serverless architectures, as well as MLOps practices and tooling (MLflow, Vertex AI, SageMaker). Exceptional at working autonomously in a remote-first environment, with strong communication skills. Benefits: Fully remote, flexible working model with a supportive and collaborative team culture. More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

mentoring and managing data science teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory #J-18808-Ljbffr More ❯

Expertise in Gen AI, including Large Language Models (LLMs) and RAG-based solution approaches Strong understanding of AI/ML tools and platforms such as Azure, AWS, GCP, Databricks, MLFlow and Streamlit Proficiency in Python and experience with frameworks like Airflow, Plotly Dash or similar tools Deep understanding of CPGR challenges, including supply chain dynamics, consumer behavior analytics and retail More ❯

in deploying and managing AI/ML models in financial systems Proficiency in Python and familiarity with AI/ML tools and platforms such as Azure, AWS, GCP, Databricks, MLFlow, Airflow and financial-specific platforms like Bloomberg Terminal, SAS, or MATLAB Experience with structured and unstructured financial data, including time-series analysis, market data and transactional data Ability to articulate More ❯

in deploying and managing AI/ML models in financial systems Proficiency in Python and familiarity with AI/ML tools and platforms such as Azure, AWS, GCP, Databricks, MLFlow, Airflow and financial-specific platforms like Bloomberg Terminal, SAS, or MATLAB Experience with structured and unstructured financial data, including time-series analysis, market data and transactional data Ability to articulate More ❯

as TensorFlow, PyTorch, Scikit-learn, and Keras. Understanding of algorithms and techniques for supervised and unsupervised learning. Experience with tools for model monitoring, logging, and performance evaluation, such as MLflow or Prometheus. Strong scripting skills in Bash, PowerShell, or similar scripting languages for automation of tasks and ability to write reusable and maintainable code to streamline ML operations Proficient in More ❯

and API designs for ML model deployments. Strong Python skills, particularly in relevant data libraries. Cloud engineering experience, particularly with AWS and Databricks. Additional experience in GenAI/NLP, MLflow, Jenkins, workflow automation, AutoML, unit testing, and model explainability is a plus! This is your chance to elevate your career and work on impactful ML projects! If you're excited More ❯

development lifecycle with a strong focus on performance and maintainability. Collaborate cross-functionally with consulting and engineering teams to guide best practices. Drive innovation using tools such as Terraform, MLflow, AzureML, LangSmith, and more. Technical Requirements: Advanced proficiency in Python and modern software engineering practices. Experience architecting solutions using major cloud platforms (Azure, AWS, GCP). Familiarity with technologies such More ❯

including: Supervised/unsupervised learning, deep learning, andnatural language processing (NLP) Model development using frameworks such asTensorFlow,PyTorch, orscikit-learn Experience deploying AI models in production environments usingMLOpsprinciples (e.g., MLflow, Azure ML, SageMaker). Hands-on experience with automation and orchestration technologies, such as: Robotic Process Automation (RPA)platforms: UiPath, Blue Prism, Automation Anywhere IT process automation (ITPA)tools: ServiceNow More ❯

Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of public sector data requirements and compliance Outstanding communication More ❯

of cutting-edge techniques for Natural Language Processing and Computer Vision Strong grasp of basic probability concepts and machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Understanding and application of Ethical AI considerations Ready to take your career to the next level? Apply today and be part of something extraordinary! Please either apply by clicking More ❯