13 of 13 Remote 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 ❯

and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning Engineer or similar More ❯

and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning Engineer or similar 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 ❯

learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale-up settings Company Market leading financial services (fintech 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯