101 to 125 of 321 MLflow Jobs in England

Dask. Strong in Python, Scala, C# or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using Bash, PowerShell, or Azure CLI, Terraform, Helm 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 ❯

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 ❯

open source capabilities on public and private cloud infrastructure, Linux and Kubernetes. Our team applies expert insights to real-world customer problems, enabling the enterprise adoption of Ubuntu, Kubeflow, MLFlow, Feast, DVC and related analytics, machine learning and data technologies. We are working to create the world's best open source data platform, covering traditional SQL databases and today's More ❯

open source capabilities on public and private cloud infrastructure, Linux and Kubernetes. Our team applies expert insights to real-world customer problems, enabling the enterprise adoption of Ubuntu, Kubeflow, MLFlow, Feast, DVC and related analytics, machine learning and data technologies. We are working to create the world's best open source data platform, covering traditional SQL databases and today's More ❯

ll do: Build and deploy scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional, real-time data processing for … ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design efficient ML pipelines and leverage cloud services like AWS to ensure reliable, scalable, and cost More ❯

and semantic similarity. Strong proficiency in Python, including use of ML libraries such as TensorFlow, PyTorch, or similar. Experience with data science tools and platforms (e.g., Jupyter, Pandas, NumPy, MLFlow). Familiarity with cloud-based AI tools and infrastructure, especially within the AWS ecosystem (SageMaker, Lambda, etc.). Strong understanding of data structures, algorithms, and statistical analysis. Experience working with More ❯

attention to detail, excellent communication skills, and a team-oriented mindset. Preferred qualifications, capabilities, and skills Master's degree in computer science, ML, or related areas. Experience with Ray, MLFlow, and/or other distributed training frameworks. Understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep knowledge of LLM techniques, including Agents, Planning, Reasoning. Experience More ❯

understanding of the marketing ecosystem, including media measurement solutions like media mix modelling. Experience with RNNs, NLP, Computer Vision, GenAI, CausalAI, GraphAI, and advanced techniques. Familiarity with versioning models (MLFlow), API design (FastAPI), and building custom dashboards (Dash). Why You’ll Love It Here: Variety and Challenge: No two projects are the same. You’ll work across multiple industries More ❯

and model performance Cloud and MLOps for Optimization Models: Familiarity with deploying and managing optimization models on cloud platforms (AWS, GCP, Azure) and employing MLOps practices (with tools like MLFlow, BentoML) to ensure efficient lifecycle management of optimization solutions Ethical AI and Continuous Learning: A robust understanding of AI ethics and privacy considerations, especially relevant to optimization, coupled with a More ❯

of data governance, compliance, and security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE ARE your future. More details More ❯

of data governance, compliance, and security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE ARE your future. Full time More ❯

databases such as SQL/NoSQL Experience with at least one Cloud Provider (AWS, Azure or GCP) Strong experience deploying and monitoring machine learning (MLOps), using tools such as MLflow, AWS Sagemaker, and Azure Machine Learning Experience in relevant Data Manipulation, Machine Learning and Statistical Analysis coding packages (eg. in Python: NumPy, Scikit-Learn, Pandas, Matplotlib etc.) Strong skills in More ❯