1 to 25 of 196 MLflow Jobs in England

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 ...

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). ...

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 ...

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 ...

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 ...

statistical evaluation Proven track record transitioning models from experimentation to production with appropriate governance and quality controls Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures It Would Be Great If You Also ...

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 ...

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 ...

algorithms. Consulting Experience: Proven track record of delivering solutions in a client-facing consulting environment. MLOps Expertise: Hands-on experience with tools such as MLflow, Kubeflow, TensorFlow Serving, or similar. Cloud Platforms: Proficiency in multi-cloud environments (AWS, Azure, GCP). DevOps & Automation: Strong knowledge of CI/CD pipelines ...

algorithms. Consulting Experience: Proven track record of delivering solutions in a client-facing consulting environment. MLOps Expertise: Hands-on experience with tools such as MLflow, Kubeflow, TensorFlow Serving, or similar. Cloud Platforms: Proficiency in multi-cloud environments (AWS, Azure, GCP). DevOps & Automation: Strong knowledge of CI/CD pipelines ...

algorithms. Consulting Experience: Proven track record of delivering solutions in a client-facing consulting environment. MLOps Expertise: Hands-on experience with tools such as MLflow, Kubeflow, TensorFlow Serving, or similar. Cloud Platforms: Proficiency in multi-cloud environments (AWS, Azure, GCP). DevOps & Automation: Strong knowledge of CI/CD pipelines ...