51 to 69 of 69 MLflow Jobs in the UK excluding London

Strategic IT project, with ongoing contract extensions. Core Technical Skills Programming: Python Data Handling: NumPy, Pandas, SQL Version Control: Git/GitHub System Integration & Deployment Model Deployment: Flask, FastAPI, MLflow Model Serving: Triton Inference Server, Hugging Face Inference Endpoints API Integration: OpenAI, Anthropic, Cohere, Mistral APIs LLM Frameworks: LangChain, LlamaIndex – for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

engineers, and stakeholders to translate business requirements into technical solutions. Optimize and deploy models using tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT. Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines. Work with large-scale data using PySpark and integrate models into production environments. Monitor model performance and retrain as needed to ensure accuracy and efficiency. … such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability … such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability More ❯

detection and failure prediction. Analyze sensor data and operational logs to support predictive maintenance strategies. Develop and maintain data pipelines using tools like Apache Airflow for efficient workflows. Use MLflow for experiment tracking, model versioning, and deployment management. Contribute to data cleaning, feature engineering, and model evaluation processes. Collaborate with engineers and data teams to better understand equipment behavior and … etc.). Solid understanding of machine learning concepts and algorithms . Interest in working with real-world industrial or sensor data . Exposure to Apache Airflow and/or MLflow (through coursework or experience) is a plus. A proactive, analytical mindset with a willingness to learn and collaborate. Why Join Us Work on meaningful AI applications that have a tangible More ❯

Role Title: Sr.Databricks Engineer Location: Glasgow Days on site: 2-3 Pay Rate: £405 per day MUST BE PAYE THROUGH UMBRELLA Role Description: We are currently migrating our data pipelines from AWS to Databricks, and are seeking a Senior Databricks More ❯

We are a Global Recruitment specialist that provides support to the clients across EMEA, APAC, US and Canada. Role Title: Sr.Databricks Engineer (AWS) Location: Glasgow Duration: 31/12/2026 Days on site: 2-3 Rate: £402/day More ❯

What You’ll Do Architect and develop backend systems (Python, FastAPI, Flask, NodeJS) Design and implement RAG pipelines (LangChain, Qdrant, embeddings) Build ETL & CI/CD workflows (Airflow, dbt, MLFlow) Integrate AI/LLM components into backend services Ensure reliability, scalability, and maintainability across systems Collaborate with a small, elite team of engineers and AI researchers 💼 About You 4+ years … of backend experience (Python, distributed systems, async APIs) Proven experience integrating AI or LLM systems (LangChain, vector DBs, etc.) Hands-on with some of Airflow, dbt, MLFlow, ETL, and CI/CD workflows Experience in early-stage startup environments — self-starter mindset Excited by ambiguity, ownership, and building from zero to one Ideally: background in workflow automation, process analytics, or More ❯

What You’ll Do Architect and develop backend systems (Python, FastAPI, Flask, NodeJS) Design and implement RAG pipelines (LangChain, Qdrant, embeddings) Build ETL & CI/CD workflows (Airflow, dbt, MLFlow) Integrate AI/LLM components into backend services Ensure reliability, scalability, and maintainability across systems Collaborate with a small, elite team of engineers and AI researchers 💼 About You 4+ years … of backend experience (Python, distributed systems, async APIs) Proven experience integrating AI or LLM systems (LangChain, vector DBs, etc.) Hands-on with some of Airflow, dbt, MLFlow, ETL, and CI/CD workflows Experience in early-stage startup environments — self-starter mindset Excited by ambiguity, ownership, and building from zero to one Ideally: background in workflow automation, process analytics, or More ❯

What You’ll Do Architect and develop backend systems (Python, FastAPI, Flask, NodeJS) Design and implement RAG pipelines (LangChain, Qdrant, embeddings) Build ETL & CI/CD workflows (Airflow, dbt, MLFlow) Integrate AI/LLM components into backend services Ensure reliability, scalability, and maintainability across systems Collaborate with a small, elite team of engineers and AI researchers 💼 About You 4+ years … of backend experience (Python, distributed systems, async APIs) Proven experience integrating AI or LLM systems (LangChain, vector DBs, etc.) Hands-on with some of Airflow, dbt, MLFlow, ETL, and CI/CD workflows Experience in early-stage startup environments — self-starter mindset Excited by ambiguity, ownership, and building from zero to one Ideally: background in workflow automation, process analytics, or More ❯