13 of 13 Permanent MLflow Jobs in the South East

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability 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 ❯

Skills: Experience using R and NLP or 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 knowledge of Excel More ❯

Skills: Experience using R and NLP or 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 knowledge of Excel More ❯

Skills: Experience using R and NLP or 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 knowledge of Excel 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 ❯

and act on. Key requirements: MSc or BSc in Computer Science, Data Science, Bioinformatics, Engineering, or a related field, or equivalent experience. Proven experience designing and deploying MLOps pipelines (MLflow, Azure ML, Azure DevOps etc). Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.). Experience implementing machine learning and More ❯

Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks such as PyTorch or Lightning Capable More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯