76 to 100 of 144 MLflow Jobs in the UK

native environments Strong technical background in data engineering, analytics, or science Experience working with Finance and Risk teams Familiar with AWS and modern data/AI stacks (SQL, Python, MLflow, MLOps) Strategic mindset with ability to scale teams and influence at C-level More ❯

analytics, or data science. Hands-on familiarity with modern data stacks – SQL, dbt, Airflow, Snowflake, Looker/Power BI. Understanding of the AI/ML lifecycle – including tooling (Python, MLflow) and best-practice MLOps. Comfortable working across finance, risk, and commercial functions. Experience operating in a regulated environment, ideally with exposure to dual regulation and resilience frameworks. Please note - this More ❯

analytics, or data science. Hands-on familiarity with modern data stacks – SQL, dbt, Airflow, Snowflake, Looker/Power BI. Understanding of the AI/ML lifecycle – including tooling (Python, MLflow) and best-practice MLOps. Comfortable working across finance, risk, and commercial functions. Experience operating in a regulated environment, ideally with exposure to dual regulation and resilience frameworks. Please note - this More ❯

backend development. Solid understanding of data processing and engineering workflows. Experience building APIs or services to support data or ML applications. Familiarity with ML model lifecycle and tooling (e.g. MLflow, Airflow, Docker). Strong problem-solving skills and the ability to work autonomously in a dynamic environment. DESIRABLE SKILLS Experience supporting LLM training or retrieval-augmented generation (RAG). Familiarity More ❯

/CD pipelines, unit testing, use of version control systems. Dashboarding and Data Visualization Skills (e.g. Streamlit, Dash, Retool, Plotly) Exposure to ML libraries/systems (Scikit-learn, PyTorch, MLflow) Clinical trials Good Clinical Practice (GCP) Medical device development About Us All our benefits information can be found in the downloadable Benefits document under 'Information' on the right hand side More ❯

solutions using cutting-edge tools like LangGraph, FastAPI, and HuggingFace. Own the full AI product lifecycle - from idea to production. Architect intelligent systems using Python, microservices, and MLOps tooling (MLflow, DVC). Work closely with cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/DL concepts. More ❯

solutions using cutting-edge tools like LangGraph, FastAPI, and HuggingFace. Own the full AI product lifecycle - from idea to production. Architect intelligent systems using Python, microservices, and MLOps tooling (MLflow, DVC). Work closely with cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/DL concepts. More ❯

to the delivery of complex business cloud solutions. The ideal candidate will have a strong background in Machine Learning engineering and an expert in operationalising models in the Databricks MLFlow environment (chosen MLOps Platform). Responsibilities: Collaborate with Data Scientists and operationalize the model with auditing enabled, ensure the run can be reproduced if needed. Implement Databricks best practices in More ❯

research Masters level) and/or industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience with ML toolkits, e.g. PyTorch More ❯

Deep experience deploying ML models into production environments Proficiency in designing scalable data pipelines and real-time inference systems Understanding of modern ML tooling and frameworks (e.g., PyTorch, TensorFlow, MLflow, AWS SageMaker) Strong cross-functional collaboration skills, particularly with data science and product teams Clear communication and an ability to prioritize for both experimentation and reliability Bonus Familiarity with optimization More ❯

Deep experience deploying ML models into production environments Proficiency in designing scalable data pipelines and real-time inference systems Understanding of modern ML tooling and frameworks (e.g., PyTorch, TensorFlow, MLflow, AWS SageMaker) Strong cross-functional collaboration skills, particularly with data science and product teams Clear communication and an ability to prioritize for both experimentation and reliability Bonus Familiarity with optimization More ❯

workflows. Cloud AI (AWS/Azure): Experience deploying AI solutions on cloud platforms. Python: Proficient in writing clean, production-quality code. AI Model Management: Familiarity with platforms such as MLFlow, Hugging Face, or LangChain. Data Processing: Experience with Databricks/Spark. SQL: Solid querying and data preparation skills. Data Architectures: Understanding of modern data systems (lakehouses, data lakes). Additional More ❯

command of the English language. Nice-to-Have Alongside the essential prerequisites, we're excited to find experience in version control of data and artefacts (like DeltaLake, DVC, or MLFlow), designing and operating cloud-based systems, preferably within AWS or Databricks, familiarity with messaging platforms and proficiency in infrastructure as code (e.g. Terraform, CloudFormation, Pulumi), a grasp of K8s and More ❯

interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines (e.g., GitLab Actions, Apache Airflow), experiment tracking (MLflow), and model monitoring for reliable production workflows; Cross-Functional Collaboration: Participate in code reviews, architectural discussions, and sprint planning to deliver features end-to-end. Requirements: Master's degree in More ❯

both on-prem, self-managed systems and also leverage AWS infrastructure. Essential: Experience of managing an on-prem Kubernetes clusters. Working knowledge of Kubeflow and other systems such as MLflow Exposure to cloud based systems e.g. AWS, Azure or GCP (they use AWS) Knowledge of Linux systems including scripting and system configuration. Understanding of how ML tools fit into MLOps More ❯

past, ideally centred around a software product, and have solid Python coding skills, and expertise with cloud infrastructure (preferably AWS). Familiarity with Containers and MLE tools such as MLflow and Airflow is essential, with any knowledge of AI SaaS or GenAI APIs being is a bonus. But what truly matters is your passion for learning and advancing technology. In More ❯

past, ideally centred around a software product, and have solid Python coding skills, and expertise with cloud infrastructure (preferably AWS). Familiarity with Containers and MLE tools such as MLflow and Airflow is essential, with any knowledge of AI SaaS or GenAI APIs being is a bonus. But what truly matters is your passion for learning and advancing technology. In More ❯

Data Scientist, preferably within the healthcare, biotech, or pharmaceutical space. Strong Python coding skills and expertise with cloud infrastructure (AWS preferred) are essential, as is familiarity with tools like MLflow and Airflow. Additional knowledge of Gen AI APIs or AI SaaS solutions is a plus. Above all, theyre seeking someone passionate about harnessing machine learning to revolutionise healthcare. In return More ❯

of delivering high-impact machine learning models and developing algorithms that solve real-world challenges Experience programming in Python, SQL and using ML platforms and frameworks such as Sagemaker, MLflow, Seldon Core or similar Prior experience or interest in working with geospatial data Technologies we use ️ Programming languages: SQL, Python, LookML, (+ Go for other backend services) Development tools and More ❯

Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Experience with AI governance and responsible AI practices Understanding of public sector data requirements and compliance Outstanding communication and stakeholder management skills Demonstrated ability to More ❯

techniques. Have experience with Cloud infrastructure (ideally AWS), DevOps technologies such as Docker or Terraform and CI/CD processes and tools. Have previously worked with MLOps tools like MLFlow and Airflow, or on common problems such as model and API monitoring, data drift and validation, autoscaling, access permissions Have previously worked with monitoring tools such as New Relic or … associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/CD: Jenkins, Github Actions More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase More ❯

techniques. Have experience with Cloud infrastructure (ideally AWS), DevOps technologies such as Docker or Terraform and CI/CD processes and tools. Have previously worked with MLOps tools like MLFlow and Airflow, or on common problems such as model and API monitoring, data drift and validation, autoscaling, access permissions... Have previously worked with monitoring tools such as New Relic or … associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc...) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/CD: Jenkins, Github Actions More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase More ❯

software engineering and AI/ML development, including: Experience with production software and systems design. Knowledge of machine learning algorithms and model development. Experience with ML lifecycle tools like MLflow, DVC, Weights & Biases. Experience deploying ML systems on the cloud. Professional experience with LLMs and large-scale models. Strong software engineering skills with scalable, distributed ML systems. Excellent analytical and More ❯

logistics, marketplaces, or similarly complex operational businesses Experience using LLMs or AI tools to structure and extract meaning from unstructured data Experience automating workflows and deploying model pipelines (e.g. MLFlow, GCP Vertex, Airflow, dbt or similar) Exposure to business planning, pricing, or commercial decision-making Familiarity with geospatial data Experience in fast-scaling startups or operational teams We're flexible More ❯

This is your opportunity to join AXIS Capital - a trusted global provider of specialty lines insurance and reinsurance. We stand apart for our outstanding client service, intelligent risk taking and superior risk adjusted returns for our shareholders. We also proudly More ❯