18 of 18 Permanent MLflow Jobs in the UK excluding London

with PyTorch, TensorFlow, Scikit-learn, and transformer-based models. Practical knowledge of LLM integration (e.g., GPT, Claude) and RAG architecture. Experience with ML lifecycle management tools like AWS SageMaker, MLflow, or Databricks. Working knowledge of CUDA, Nvidia GPUs, and distributed training. Experience with AWS services (S3, Lambda, EC2, SageMaker, Bedrock, etc.). Desired: Experience deploying models as APIs/microservices More ❯

technical teams, ideally in cloud-first environments (Azure, AWS, or GCP) Proficiency in Python, SQL , and cloud-native data tools Solid understanding of MLOps , including model lifecycle management (e.g. MLflow), containers (Docker/Kubernetes), and monitoring Experience delivering Data-as-a-Service products and APIs Excellent communication skills - able to explain complex concepts to both technical and non-technical audiences More ❯

workflows, containerisation (e.g., Docker, Kubernetes) and production-grade APIs Understanding of data governance, privacy and regulatory compliance (e.g., GDPR) Nice to have: Familiarity with Infrastructure as Code (e.g., Ansible), MLFlow, or orchestration frameworks Background in both object-oriented and functional programming paradigms Please note: Visa sponsorship is unfortunately not available for this role. Applicants must have the right to work More ❯

foundational LLMs to custom built cognitive agents, solving real world business challenges. Establishing scalable ML/AI pipelines and infrastructure, working closely with data engineering and DevOps functions (Kubernetes, MLflow, Vertex AI, etc.). Acting as a thought leader, identifying new AI use cases and communicating technical vision to executive stakeholders and cross-functional teams. Champion ethical AI principles, ensuring More ❯

in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with data warehouse tools (Snowflake, Redshift, BigQuery) Familiarity with search, matching, or recommendation More ❯

relevant academic (research Masters level) and/or industry experience. 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 ❯

including APIs, data structures, and async processing* Databricks/Microsoft Fabric* Cloud, preferably Azure (Data Lake, Functions, App Services)* Containerisation with Docker and CI/CD pipelines* MLOps tooling (MLFlow, Git-based versioning, environment tracking)Desirable Skills & Interests* LangChain, Langflow, or similar frameworks for building AI agents* LLMs or intelligent automation workflows* High-availability, scalable systems (microservices, event-based architectures More ❯

background in data engineering, analytics, or data science. Experience with modern data stacks (e.g., SQL, dbt, Airflow, Snowflake, Looker/Power BI) and AI/ML tooling (e.g., Python, MLflow, MLOps). A track record of building and managing high-performing data teams. Strategic thinking and ability to influence senior stakeholders, comfortable in dual-regulated environments. HOW TO APPLY: Please More ❯

call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into ML Ops workflows Design and 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 ❯

call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into ML Ops workflows Design and More ❯

PyTorch Knowledge of Natural Language Processing (NLP) and Computer Vision techniques Strong understanding of probability concepts and the machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Awareness and application of Ethical AI principles This role offers a unique opportunity to work on cutting-edge projects, expand your expertise, and contribute to innovative AI and data More ❯

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 ❯

/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 ❯

modern computer vision models and techniques such as ResNet, YOLO, Vision Transformers (ViTs), or similar. Solid understanding of image processing workflows, deep learning pipelines, and model evaluation. Familiarity with MLflow for tracking experiments, managing model lifecycle, or deploying models. Experience with AWS services such as S3, EC2, SageMaker, Lambda, or similar tools for model deployment and data pipelines. What you More ❯

modal models that combine vision and language Strong grasp of data-centric AI practices - annotation tooling, prompt evaluation, and dataset curation Familiarity with MLOps tools (e.g. Weights & Biases, SageMaker, MLflow) Experience working in regulated sectors like insurance, banking, or property What You'll Be Doing This is a hands-on, high-impact role - you'll be building production-grade AI More ❯

Communication and Problem-Solving: Strong analytical and debugging skills, with the ability to clearly communicate technical decisions and collaborate across disciplines. Experience and skills we'd love: Familiarity with MLflow for tracking experiments, managing model lifecycle, or deploying models. Experience with AWS services such as S3, EC2, SageMaker, Lambda, or similar tools for model deployment and data pipelines. Computer Vision More ❯

Cloud services (Data Lake, Functions, App Services) for development and deployment Implement and maintain containerized solutions using Docker and CI/CD pipelines Apply MLOps practices using tools like MLFlow , Git-based versioning, and environment tracking Build and integrate AI and intelligent automation frameworks Ensure applications meet performance, reliability, and scalability requirements Required Skills & Experience: 2+ years of Python development … Proficiency in Azure cloud services including Data Lake, Functions, and App Services Strong understanding of Docker , CI/CD workflows, and automation pipelines Familiarity with MLOps tooling such as MLFlow, Git version control, and environment management Desirable Skills & Interests: Experience with frameworks like LangChain , Langflow , or similar tools for building AI agents Understanding of Large Language Models (LLMs) and intelligent More ❯