1 to 25 of 152 MLflow Jobs in the UK

reproducibility, and versioning. 4. MLOps & Automation Implement MLOps practices including CI/CD for ML, model versioning, and experiment tracking. Use tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML. Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such ...

workflows. ML Ops & deployment: Familiarity with deploying models into production using APIs or microservices, and applying ML Ops practices such as experiment tracking (e.g., MLflow, Weights & Biases), model versioning, and performance monitoring. Experience collaborating with engineering teams to ensure scalable and maintainable deployment. Backend & service development: Experience building production-grade ...

workflows. ML Ops & deployment: Familiarity with deploying models into production using APIs or microservices, and applying ML Ops practices such as experiment tracking (e.g., MLflow, Weights & Biases), model versioning, and performance monitoring. Experience collaborating with engineering teams to ensure scalable and maintainable deployment. Backend & service development: Experience building production-grade ...

Experience with data warehouses such as Snowflake, BigQuery or Redshift Workflow orchestration using tools like Airflow or Dagster Experience with MLOps tools such as MLflow, Weights and Biases or similar Awareness of data and model drift, and how to monitor and respond to it before it erodes value Soft skills ...

Experience with data warehouses such as Snowflake, BigQuery or Redshift Workflow orchestration using tools like Airflow or Dagster Experience with MLOps tools such as MLflow, Weights and Biases or similar Awareness of data and model drift, and how to monitor and respond to it before it erodes value Security ...

Solid understanding of supervised/unsupervised learning, deep learning, and model evaluation techniques. Cloud & MLOps: Experience with AWS, GCP, or Azure; Docker/Kubernetes; MLflow or similar tools. Data Handling: Familiarity with big data frameworks (Spark, Hadoop) and data versioning tools (DVC). Soft Skills: Strong problem-solving, communication ...

Solid understanding of supervised/unsupervised learning, deep learning, and model evaluation techniques. Cloud & MLOps: Experience with AWS, GCP, or Azure; Docker/Kubernetes; MLflow or similar tools. Data Handling: Familiarity with big data frameworks (Spark, Hadoop) and data versioning tools (DVC). Soft Skills: Strong problem-solving, communication ...

Solid understanding of supervised/unsupervised learning, deep learning, and model evaluation techniques. Cloud & MLOps: Experience with AWS, GCP, or Azure; Docker/Kubernetes; MLflow or similar tools. Data Handling: Familiarity with big data frameworks (Spark, Hadoop) and data versioning tools (DVC). Soft Skills: Strong problem-solving, communication ...

data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding ...

data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding ...

data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding ...

data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding ...

Experience: Experience with generative AI and large language models, including LangChain, vector databases and retrieval‐augmented generation Knowledge of AutoML, hyperparameter optimisation and advanced MLflow features. Experience working with streaming or real‐time data platforms Familiarity with CI/CD, MLOps and deployment pipelines (Azure DevOps, GitHub Actions, Docker). ...

Experience: Experience with generative AI and large language models, including LangChain, vector databases and retrieval‐augmented generation Knowledge of AutoML, hyperparameter optimisation and advanced MLflow features. Experience working with streaming or real‐time data platforms Familiarity with CI/CD, MLOps and deployment pipelines (Azure DevOps, GitHub Actions, Docker). ...

Experience: Experience with generative AI and large language models, including LangChain, vector databases and retrieval‐augmented generation Knowledge of AutoML, hyperparameter optimisation and advanced MLflow features. Experience working with streaming or real‐time data platforms Familiarity with CI/CD, MLOps and deployment pipelines (Azure DevOps, GitHub Actions, Docker). ...

Experience: Experience with generative AI and large language models, including LangChain, vector databases and retrieval‐augmented generation Knowledge of AutoML, hyperparameter optimisation and advanced MLflow features. Experience working with streaming or real‐time data platforms Familiarity with CI/CD, MLOps and deployment pipelines (Azure DevOps, GitHub Actions, Docker). ...

statistical evaluation Proven experience moving models from experimentation into production with appropriate governance and quality controls Hands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG) Familiarity with LLMOps frameworks ...

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 ...

Infrastructure as Code (Terraform), Docker - containerisation. An understanding of kubernetes. Understanding of LLM architectures, inference serving optimization techniques (quantisation, caching), and MLOps tooling (e.g., MLFlow, Kubeflow). Proven experience designing, implementing, and optimising complex RAG pipelines using vector databases (e.g., Pinecone, PGVector). Practical experience with agentic frameworks (e.g., LangChain ...

Infrastructure as Code (Terraform), Docker - containerisation. An understanding of kubernetes. Understanding of LLM architectures, inference serving optimization techniques (quantisation, caching), and MLOps tooling (e.g., MLFlow, Kubeflow). Proven experience designing, implementing, and optimising complex RAG pipelines using vector databases (e.g., Pinecone, PGVector). Practical experience with agentic frameworks (e.g., LangChain ...

Python (pandas, NumPy, scikit-learn; PyTorch/TF a plus). Feature engineering, data quality validation, model training and optimisation. MLOps: experiment tracking (eg, MLflow), model registry, CI/CD for ML. Serving and orchestration: AWS SageMaker, EKS, batch & Real Time inference. Data pipelines: Spark/SQL, orchestration (Airflow), monitoring ...

Python (pandas, NumPy, scikit-learn; PyTorch/TF a plus). Feature engineering, data quality validation, model training and optimisation. MLOps: experiment tracking (e.g., MLflow), model registry, CI/CD for ML. Serving and orchestration: AWS SageMaker, EKS, batch & real-time inference. Data pipelines: Spark/SQL, orchestration (Airflow), monitoring ...

scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with MLflow, Weights & Biases, and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data ...

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, or Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large, complex organisations or regulated industries. Strong working ...

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, or Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large, complex organisations or regulated industries. Strong working ...