101 to 125 of 338 MLflow Jobs in the UK

Strong understanding of SQL, NoSQL, and data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative mindset and ability to work in a fast-paced, small team More ❯

Strong understanding of SQL, NoSQL, and data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative mindset and ability to work in a fast-paced, small team More ❯

architectures and tools like Delta Lake , LakeFS , or Databricks . Knowledge of security and compliance best practices (e.g., SOC2, ISO 27001). Exposure to MLOps platforms or frameworks (e.g., MLflow, Kubeflow, Vertex AI). What We Offer Competitive salary + equity Flexible work environment and remote-friendly culture Opportunities to work on cutting-edge AI/ML technology Fast-paced More ❯

Strong understanding of SQL, NoSQL, and data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative mindset and ability to work in a fast-paced, small team More ❯

stack. Knowledge and experience of development and version control tools and workflows (e.g. Git, Feature branch). Experience of MLOps and associated tools such as Azure DevOps/Github, MLFlow, Azure ML. Experience working with large datasets/big data architectures, particularly Pyspark/Databricks. Experience deploying container technologies (e.g. Docker, Kubernetes). Experience playing a lead role on technical More ❯

pipelines and tooling. Proficiency in Python and experience with ML libraries (e.g., PyTorch, TensorFlow, scikit-learn). Experience with the Go programming language Experience with experiment tracking tools (e.g., MLflow, Weights & Biases). Strong knowledge of DevOps, CI/CD practices, and cloud infrastructure (AWS preferred). Excellent communication and collaboration skills, with the ability to work effectively across teams. More ❯

S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation . Experience implementing and scaling MLOps workflows with tools such as MLflow, SageMaker Pipelines . Proven experience building, containerising, and deploying using Docker and Kubernetes . Hands-on experience with CI/CD tools ( GitHub Actions , CodePipeline , Jenkins ) and version control using More ❯

Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps workflows with tools such as MLflow, SageMaker Pipelines. Proven experience building, containerising, and deploying using Docker and Kubernetes. Hands-on experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins) and version control using Git/ More ❯

Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps workflows with tools such as MLflow, SageMaker Pipelines. Proven experience building, containerising, and deploying using Docker and Kubernetes. Hands-on experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins) and version control using Git/ More ❯

Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps workflows with tools such as MLflow, SageMaker Pipelines. Proven experience building, containerising, and deploying using Docker and Kubernetes. Hands-on experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins) and version control using Git/ More ❯

Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps workflows with tools such as MLflow, SageMaker Pipelines. Proven experience building, containerising, and deploying using Docker and Kubernetes. Hands-on experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins) and version control using Git/ More ❯

Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps workflows with tools such as MLflow, SageMaker Pipelines. Proven experience building, containerising, and deploying using Docker and Kubernetes. Hands-on experience with CI/CD tools (GitHub Actions, CodePipeline, Jenkins) and version control using Git/ More ❯

roles. Proven track record in designing and deploying scalable machine learning systems in production. Strong programming skills in Python and experience with ML frameworks and tools (e.g., TensorFlow, PyTorch, MLFlow, Kubeflow, Jupyter, Azure ML Studio, Amazon Sagemaker, Apache Spark, Apache Flink). Expertise in containerization (e.g., Docker, Kubernetes) and automation (e.g., Jenkins, GitLab CI). Excellent problem-solving skills and More ❯

and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing More ❯

and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing More ❯

objectives. 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 ❯

objectives. 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 ❯

and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing More ❯

and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale Understanding of model performance metrics, calibration, A/B testing More ❯

prompt engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured More ❯

prompt engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured More ❯

prompt engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured More ❯

Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, extra festive time off, and excellent family-friendly More ❯

prompt engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured More ❯

Demonstrated experience delivering AI solutions in production cloud environments. Advanced Python: Expertise in developing efficient, production-grade AI/ML code. LLMOps & AI Model Management: Experience with tools like MLFlow, LangChain, Hugging Face, Kubeflow, or similar platforms. Data Processing: Proficient with Databricks/Spark for large-scale AI data processing. SQL: Strong capabilities in data querying and preparation. Data Architectures More ❯