1 to 25 of 67 MLflow Jobs in the UK excluding London

TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and More ❯

TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and More ❯

TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and More ❯

AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting for automation and tooling development. Version control More ❯

AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting for automation and tooling development. Version control More ❯

Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow, CI/CD pipelines Bonus: Experience with wearable/sensor data, player tracking, or sports video analytics TO BE CONSIDERED... Please apply directly by emailing jordanna.ramsey@searchability.com with your CV More ❯

Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow, CI/CD pipelines Bonus: Experience with wearable/sensor data, player tracking, or sports video analytics TO BE CONSIDERED... Please apply directly by emailing jordanna.ramsey@searchability.com with your CV More ❯

Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow, CI/CD pipelines Bonus: Experience with wearable/sensor data, player tracking, or sports video analytics TO BE CONSIDERED... Please apply directly by emailing jordanna.ramsey@searchability.com with your CV More ❯

ML, data engineering, or AI systems development Strong skills in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL Experience with cloud platforms and modern ML infrastructure (e.g. Databricks, MLflow, Docker, CI/CD) A practical mindset, curious, experimental, and focused on real-world delivery Why Join Work with a modern AI/ML stack (Python, SQL, Databricks, cloud-first More ❯

learn, PyTorch, TensorFlow, and XGBoost. Design robust data pipelines using tools like Spark and Kafka for real-time and batch processing. Manage ML lifecycle with tools such as Databricks , MLflow , and cloud-native platforms (Azure preferred). Collaborate with engineering teams to ensure scalable, secure ML infrastructure aligned with compliance standards (e.g., ISO27001). Ensure data governance, particularly around sensitive More ❯

such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability More ❯

Databricks Unity Catalog and Delta Live Tables. Familiarity with streaming frameworks (Structured Streaming, Kafka, etc.). Background in data observability and metadata management. Exposure to machine learning pipelines or MLflow within Databricks. Knowledge of infrastructure as code (IaC) using Terraform or similar tools. To apply for this role please email across your CV ASAP. More ❯

Databricks Unity Catalog and Delta Live Tables. Familiarity with streaming frameworks (Structured Streaming, Kafka, etc.). Background in data observability and metadata management. Exposure to machine learning pipelines or MLflow within Databricks. Knowledge of infrastructure as code (IaC) using Terraform or similar tools. To apply for this role please email across your CV ASAP. More ❯

Databricks Unity Catalog and Delta Live Tables. Familiarity with streaming frameworks (Structured Streaming, Kafka, etc.). Background in data observability and metadata management. Exposure to machine learning pipelines or MLflow within Databricks. Knowledge of infrastructure as code (IaC) using Terraform or similar tools. To apply for this role please email across your CV ASAP. More ❯

strong software engineering (testing, type hints, code review, packaging). Cloud (AWS/Azure/GCP), CI/CD, Docker/Kubernetes; workflow orchestration (Airflow/Prefect); experiment tracking (MLflow/W&B). Machine Learning for time series/tabular data; LLM/agentic systems, retrieval pipelines, and evaluation/guardrails. Research driven, rigorous and curious; clear scientific writing More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

opportunities. Nice-to-Have Experience: Experience designing or working with Human-in-the-Loop systems for data annotation or model validation. Familiarity with MLOps tools and practices (Docker, Kubernetes, MLflow, etc.). Previous experience in a startup or high-growth environment. A background in healthcare, life sciences, or a related domain, but this is not required. What We Offer The More ❯

opportunities. Nice-to-Have Experience: Experience designing or working with Human-in-the-Loop systems for data annotation or model validation. Familiarity with MLOps tools and practices (Docker, Kubernetes, MLflow, etc.). Previous experience in a startup or high-growth environment. A background in healthcare, life sciences, or a related domain, but this is not required. What We Offer The More ❯

experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). More ❯

development through testing, release, monitoring, and support. Operationalize models with CI/CD pipelines, automated testing, and monitoring, applying MLOps practices such as versioning, retraining, and drift detection (tools: MLflow, Azure ML, Databricks) Leverage both open-source frameworks (LangChain, Hugging Face, etc.) and enterprise platforms (Azure OpenAI, Databricks, etc.) to deliver production ready, scalable AI solutions Implement generative AI and More ❯

of monitoring, logging, and performance testing for GPU/ML workloads. Excellent collaboration skills — able to work with research, engineering, and product teams. Desirables: Experience in MLOps/LLMOps (MLflow, Kubeflow, Weights & Biases, experiment tracking). Exposure to video processing, compression, or neural rendering pipelines. Knowledge of FPGA/embedded deployment Contributions to open-source GPU/ML/DevOps More ❯

of monitoring, logging, and performance testing for GPU/ML workloads. Excellent collaboration skills — able to work with research, engineering, and product teams. Desirables: Experience in MLOps/LLMOps (MLflow, Kubeflow, Weights & Biases, experiment tracking). Exposure to video processing, compression, or neural rendering pipelines. Knowledge of FPGA/embedded deployment Contributions to open-source GPU/ML/DevOps More ❯

Python (e.g. Boto3, SDKs). Proven experience with Python-based IaC frameworks (Pulumi, Terraform, CDKs). Hands-on experience building CI/CD pipelines for AI deployments (Github Actions, MLFlow, ZenML, or similar). Deep understanding of containerisation and orchestration tools (Docker, Kubernetes). Desirable Experience deploying AI inference engines (vLLM, Ray Serve, Triton). Familiarity with observability tools for More ❯