18 of 18 Remote MLflow Jobs in the UK excluding London

with a focus on practical application and performance trade-offs. Experience with cloud platforms (e.g., AWS, Azure) and containerisation (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, SageMaker, Airflow). Experience working with large-scale datasets and distributed computing frameworks (e.g., Spark). Strong communication skills and ability to work collaboratively in a team environment. MSc or More ❯

in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills More ❯

in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills More ❯

in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills More ❯

experience of AGILE methodologies. Proficient with SQL. Familiarity with Databricks, Spark, geospatial data/modelling Exposure to MLOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. are a plus. Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience and potential. Discretionary bonus scheme that recognises your hard More ❯

Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Exposure to LLMOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. Knowledge of Langchain, huggingface and LLM offerings by 3rd party vendors. Knowledge of OO programming and software design, i.e., SOLID principles. Knowledge and working experience of AGILE More ❯

Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices Git, version control, unit testing and containerisation. Familiarity with agile work methodologies and tools like Jira and Confluence. Behavioural Attributes and Skills More ❯

Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices Git, version control, unit testing and containerisation. Familiarity with agile work methodologies and tools like Jira and Confluence. Behavioural Attributes and Skills More ❯

Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices Git, version control, unit testing and containerisation. Familiarity with agile work methodologies and tools like Jira and Confluence. Behavioural Attributes and Skills More ❯

of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science, Machine Learning, or related field. More ❯

of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science, Machine Learning, or related field. More ❯

of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science, Machine Learning, or related field. More ❯

experience deploying and supporting LLMs in production. Strong understanding of LLM fine-tuning (PyTorch, TensorFlow, Hugging Face Trainer, etc.). Experience with ML tooling (e.g. SageMaker, LangChain/LangSmith, MLflow, Dataiku, DataRobot). Knowledge of embeddings, their applications, and limitations. Hands-on experience in Agile/Lean/XP environments. Excellent communication, problem-solving, and cross-team collaboration skills. Proactive More ❯

with large-scale and sophisticated data sets Experience in applying machine learning to domains such as e-commerce, finance, health care, etc. Experience in using ML tools such as Mlflow for model lifecycle management. Experience in building ML models in production using AWS ecosystem, especially ML related services such as SageMaker. Familiarity with large language models (LLM). Proficiency in More ❯

Agile methodologies Familiarity with TDD , pair programming, and modern development practices Desirable: Advanced fine-tuning techniques (LoRA, QLoRA) RLHF, multi-modal AI, or edge deployment experience ML experiment tracking (MLflow, Weights & Biases) and A/B testing strategies If you possess the above technical skills and hands-on experience, please apply today! McGregor Boyall is an equal opportunity employer and More ❯

and structured data - Reinforcement Learning from Human Feedback (RLHF) for model alignment - Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management - Model versioning and experiment tracking (MLflow, Weights & Biases) - Real-time model serving and edge deployment strategies - A/B testing frameworks for ML model evaluation If you would like to hear more about this opportunity please More ❯

and structured data - Reinforcement Learning from Human Feedback (RLHF) for model alignment - Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management - Model versioning and experiment tracking (MLflow, Weights & Biases) - Real-time model serving and edge deployment strategies - A/B testing frameworks for ML model evaluation If you would like to hear more about this opportunity please More ❯

and structured data - Reinforcement Learning from Human Feedback (RLHF) for model alignment - Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management - Model versioning and experiment tracking (MLflow, Weights & Biases) - Real-time model serving and edge deployment strategies - A/B testing frameworks for ML model evaluation If you would like to hear more about this opportunity please More ❯