15 of 15 MLflow Jobs in the South East

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 ❯

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 ❯

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 ❯

relevant academic (research Masters level) and/or industry experience. Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience with ML toolkits, e.g. PyTorch More ❯

management Hands-on experience enabling AI/ML in a data platform Strong ETL/ELT engineering skills Desirable Experience with Python and related tooling Understanding of MLOps practices (MLflow, Azure ML) Familiarity with real-time data technologies (Kafka, Delta Live Tables) If you're passionate about transforming the banking industry and eager to leverage your expertise to drive continuous More ❯

management Hands-on experience enabling AI/ML in a data platform Strong ETL/ELT engineering skills Desirable Experience with Python and related tooling Understanding of MLOps practices (MLflow, Azure ML) Familiarity with real-time data technologies (Kafka, Delta Live Tables) If you're passionate about transforming the banking industry and eager to leverage your expertise to drive continuous More ❯

ethical, and regulatory requirements Required Expertise: Extensive ML Ops/ML Engineering experience, particularly in assurance, governance, and monitoring Hands-on knowledge of ML Ops platforms and tools (Kubeflow, MLflow, SageMaker, Azure ML, or equivalent) Proven ability to deploy and maintain AI solutions in regulated or complex operational settings Strong grounding in responsible AI practices, including explainability and fairness Experience More ❯

of internal ML platforms, and help identify high-impact opportunities for rapid capability delivery. You'll also be responsible for building robust MLOps pipelines using tools like Airflow and MLflow, ensuring scalable and automated model deployment and governance. What you'll need to succeed Essential Skills: * Bachelor's degree (2:1 or above) in science, engineering, mathematics, or computer science … Strong Python development skills * Practical experience with machine learning or deep learning in scientific/engineering contexts * Familiarity with MLOps tools and practices (Airflow, MLflow) and containerisation (e.g., Docker) * Passion for working with real-world datasets and data visualisation * Interest in material discovery, computer vision, big data, and optimisation techniques * Excellent communication and collaboration skills * Problem-solving mindset and self More ❯