1 to 25 of 131 Permanent MLflow Jobs in the UK

experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). What we offer Culture of caring: At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level More ❯

experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). What We Offer Culture of Caring: At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level More ❯

services. Hands-on experience with Docker, Kubernetes, and container orchestration. Expertise with Databricks, including ML workflows and data pipeline management. Familiarity with tools like MLflow, DVC, Prometheus, and Grafana for versioning and monitoring. Experience implementing security and compliance standards for AI systems. Strong problem-solving and communication skills, with a More ❯

managed AI/ML services Practical experience with Docker, Kubernetes, and container orchestration Databricks expertise, including ML workflows and data pipeline integration Familiarity with MLflow, DVC, Prometheus, and Grafana for versioning and monitoring Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience) Fluency More ❯

managed AI/ML services Practical experience with Docker, Kubernetes, and container orchestration Databricks expertise, including ML workflows and data pipeline integration Familiarity with MLflow, DVC, Prometheus, and Grafana for versioning and monitoring Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience) Fluency More ❯

managed AI/ML services Practical experience with Docker, Kubernetes, and container orchestration Databricks expertise, including ML workflows and data pipeline integration Familiarity with MLflow, DVC, Prometheus, and Grafana for versioning and monitoring Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience) Fluency More ❯

cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines, version control, and ML workflows (MLflow, Kubeflow). Proven track record of delivering ML models that solve real-world business challenges at scale. Excellent communication skills with the ability to work More ❯

Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track record of deploying machine learning models to production Strong programming skills in Python and familiarity with modern ML More ❯

Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track record of deploying machine learning models to production Strong programming skills in Python and familiarity with modern ML More ❯

cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines, version control, and ML workflows (MLflow, Kubeflow). Proven track record of delivering ML models that solve real-world business challenges at scale. Excellent communication skills with the ability to work More ❯

cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines, version control, and ML workflows (MLflow, Kubeflow). Proven track record of delivering ML models that solve real-world business challenges at scale. Excellent communication skills with the ability to work More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

Designing and fine‑tuning foundation‑model solutions; RAG patterns; prompt‑engineering & evaluation MLOps/LLMOps: CI/CD for models, feature stores, experiment tracking (MLflow, SageMaker, Azure ML, Vertex, etc.) Cloud‑Native Delivery: Building scalable AI pipelines on AWS or Azure; Terraform/IaC familiarity Python Excellence: Clean, testable code More ❯

Designing and fine‑tuning foundation‑model solutions; RAG patterns; prompt‑engineering & evaluation MLOps/LLMOps: CI/CD for models, feature stores, experiment tracking (MLflow, SageMaker, Azure ML, Vertex, etc.) Cloud‑Native Delivery: Building scalable AI pipelines on AWS or Azure; Terraform/IaC familiarity Python Excellence: Clean, testable code More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

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 More ❯

you will contribute in this role: Architecting, building and maintaining GenAI processing pipelines/workflows in Python via technologies such as serverless functions, Airflow, MLflow, SageMaker, Bedrock, or other Working with Data and NLP Engineers and Data Scientists to automate experiments that bring ML and NLP research ideas to production More ❯

large language models Proven experience with cloud platforms such as AWS, Azure, or Google Cloud Familiarity with tools and frameworks such as TensorFlow, PyTorch, MLflow, SageMaker, or Databricks Deep understanding of data architecture, APIs, and model deployment best practices Knowledge of MLOps and full model lifecycle management Excellent communication and More ❯

PostgreSQL Databricks Containerisation: Docker, Kubernetes CI/CD: Azure DevOps, GitHub Actions Relational databases and data lake architecture Model and data pipeline integration (e.g. MLflow) Microsoft Azure (Functions, Storage, Compute) Monitoring tools (Grafana, Prometheus, etc.) Mentoring and knowledge sharing within the team Senior Engineer - Desirable Skills: Experience in energy supply More ❯

PostgreSQL Databricks Containerisation: Docker, Kubernetes CI/CD: Azure DevOps, GitHub Actions Relational databases and data lake architecture Model and data pipeline integration (e.g. MLflow) Microsoft Azure (Functions, Storage, Compute) Monitoring tools (Grafana, Prometheus, etc.) Mentoring and knowledge sharing within the team Senior Engineer - Desirable Skills: Experience in energy supply More ❯

PostgreSQL Databricks Containerisation: Docker, Kubernetes CI/CD: Azure DevOps, GitHub Actions Relational databases and data lake architecture Model and data pipeline integration (e.g. MLflow) Microsoft Azure (Functions, Storage, Compute) Monitoring tools (Grafana, Prometheus, etc.) Mentoring and knowledge sharing within the team Senior Engineer - Desirable Skills: Experience in energy supply More ❯

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 More ❯