1 to 25 of 29 MLflow Jobs in the South East

TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and More ❯

TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and More ❯

TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and More ❯

AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting for automation and tooling development. Version control More ❯

AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting for automation and tooling development. Version control 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 ❯

strong software engineering (testing, type hints, code review, packaging). Cloud (AWS/Azure/GCP), CI/CD, Docker/Kubernetes; workflow orchestration (Airflow/Prefect); experiment tracking (MLflow/W&B). Machine Learning for time series/tabular data; LLM/agentic systems, retrieval pipelines, and evaluation/guardrails. Research driven, rigorous and curious; clear scientific writing More ❯

development through testing, release, monitoring, and support. Operationalize models with CI/CD pipelines, automated testing, and monitoring, applying MLOps practices such as versioning, retraining, and drift detection (tools: MLflow, Azure ML, Databricks) Leverage both open-source frameworks (LangChain, Hugging Face, etc.) and enterprise platforms (Azure OpenAI, Databricks, etc.) to deliver production ready, scalable AI solutions Implement generative AI and More ❯

of monitoring, logging, and performance testing for GPU/ML workloads. Excellent collaboration skills — able to work with research, engineering, and product teams. Desirables: Experience in MLOps/LLMOps (MLflow, Kubeflow, Weights & Biases, experiment tracking). Exposure to video processing, compression, or neural rendering pipelines. Knowledge of FPGA/embedded deployment Contributions to open-source GPU/ML/DevOps More ❯

of monitoring, logging, and performance testing for GPU/ML workloads. Excellent collaboration skills — able to work with research, engineering, and product teams. Desirables: Experience in MLOps/LLMOps (MLflow, Kubeflow, Weights & Biases, experiment tracking). Exposure to video processing, compression, or neural rendering pipelines. Knowledge of FPGA/embedded deployment Contributions to open-source GPU/ML/DevOps More ❯

orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you’ve built and scaled More ❯

orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you’ve built and scaled More ❯

orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you’ve built and scaled More ❯

We're Looking For Must Have Practical experience deploying ML models into production environments Strong Python development skills and understanding of ML model structures Familiarity with tools such as MLflow, Airflow, SageMaker, or Vertex AI Understanding of CI/CD concepts and basic infrastructure automation Ability to write well-tested, maintainable, and modular code Strong collaboration skills and a growth More ❯

Machine Learning Engineer Machine Learning Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's … in. Our client is seeking Machine Learning Engineers with experience in some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that's fusing AI More ❯

Computer Science (Russell Group or similar). Strong Python skills (Pandas, NumPy). Experience or exposure to NLP, transformers, and LLMs. Familiarity with ML tools like Weights & Biases or MLflow is a plus. Curious, proactive, and confident working independently. Ideally based near London for occasional client visits. 📩 Interested? Message me here or email mmatysik@trg-uk.com More ❯

Computer Science (Russell Group or similar). Strong Python skills (Pandas, NumPy). Experience or exposure to NLP, transformers, and LLMs. Familiarity with ML tools like Weights & Biases or MLflow is a plus. Curious, proactive, and confident working independently. Ideally based near London for occasional client visits. 📩 Interested? Message me here or email mmatysik@trg-uk.com More ❯

Computer Science (Russell Group or similar). Strong Python skills (Pandas, NumPy). Experience or exposure to NLP, transformers, and LLMs. Familiarity with ML tools like Weights & Biases or MLflow is a plus. Curious, proactive, and confident working independently. Ideally based near London for occasional client visits. 📩 Interested? Message me here or email mmatysik@trg-uk.com More ❯

difference. Using your expertise in Azure Machine Learning, Python (pandas, scikit-learn) and Azure SDKs, you will help shape our Data & Productivity capability—designing, training, and deploying models with MLflow/AutoML, and integrating them into real business processes. You'll collaborate across disciplines (Data Engineering, Security, Product) and work with services such as Azure Storage, Azure DevOps/GitHub … standards. Prepare and transform data for modelling using Python and Azure-native tools, ensuring quality and consistency across the ML and AI lifecycles. Evaluate, and deploy models using AutoML, MLflow, and custom pipelines, with a focus on performance, scalability, and maintainability. Monitor model performance, detect drift, and implement retraining strategies to maintain relevance and accuracy. Apply responsible AI principles including More ❯

deliver new capabilities Build and maintain robust MLOps pipelines to support scalable, reproducible, and automated model development, deployment, and monitoring Leverage tools such as Airflow for workflow orchestration and MLflow for experiment tracking, model registry, and lifecycle management, ensuring strong CI/CD practices and model governance Essential Skills Bachelor’s degree in science, engineering, mathematics or computer science … Practical experience in the development of machine learning models and/or deep learning to solve complex science and engineering problems Experience with MLOps tools and practices, including Airflow, MLflow, and containerization (e.g., Docker) A passion for gaining insight into real-world datasets and clearly communicating through data visualization techniques Interest in material discovery, computer vision, handling big data and More ❯

Java, or Node.js. Collaborate with architects to define scalable, secure, and cost-efficient AI service architectures. Implement AI/ML pipelines for training, validation, and deployment using tools like MLflow, Vertex AI, or Azure ML. Monitor model performance, detect drift, and drive continuous improvement. Optimize inference performance and cost through model compression, quantization, and API optimization. Ensure compliance with AI … tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree More ❯

Java, or Node.js. Collaborate with architects to define scalable, secure, and cost-efficient AI service architectures. Implement AI/ML pipelines for training, validation, and deployment using tools like MLflow, Vertex AI, or Azure ML. Monitor model performance, detect drift, and drive continuous improvement. Optimize inference performance and cost through model compression, quantization, and API optimization. Ensure compliance with AI … tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree More ❯