10 of 10 MLOps Jobs in Surrey

LLMs) and prompt engineering (e.g., GPT, BERT, T5 family). • Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). • Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. • Open-source contributions or published papers in top-tier AI/ML conferences (NeurIPS, ICML, CVPR, ACL More ❯

pipelines. Familiarity with data lake architectures and tools like Delta Lake , LakeFS , or Databricks . Knowledge of security and compliance best practices (e.g., SOC2, ISO 27001). Exposure to MLOps platforms or frameworks (e.g., MLflow, Kubeflow, Vertex AI). What We Offer Competitive salary + equity Flexible work environment and remote-friendly culture Opportunities to work on cutting-edge AI More ❯

pipelines. Familiarity with data lake architectures and tools like Delta Lake , LakeFS , or Databricks . Knowledge of security and compliance best practices (e.g., SOC2, ISO 27001). Exposure to MLOps platforms or frameworks (e.g., MLflow, Kubeflow, Vertex AI). What We Offer Competitive salary + equity Flexible work environment and remote-friendly culture Opportunities to work on cutting-edge AI More ❯

in delivering training, mentoring, or leading workshops (Trainer, Instructor, Lecturer, Coach, Mentor, Educator). Ability to explain complex topics clearly and adapt to different learning levels. Familiarity with modern MLOps practices, reproducibility, and collaborative workflows. Practical experience deploying or training models in cloud environments (AWS, GCP, Azure). This is a fully remote position offering a salary of More ❯

Trainer/Instructor/Lecturer/Teacher Consultant/Coach/Mentor/Educator ) Ability to explain complex topics clearly and adapt to different learning levels. Familiarity with modern MLOps practices, reproducibility, and collaborative workflows. Practical experience deploying or training models in cloud environments (AWS, GCP or Azure) This is a fully remote working position & salary is £65,000, with More ❯

leveraging cloud-native tools and services. AI Feature Integration Collaborate with engineering and product teams to embed AI-driven functionality into user-facing software. Technical Leadership Set standards for MLOps, automation, and performance tuning across the engineering team. Generative AI & LLMs Explore and integrate modern techniques such as large language models and generative architectures. Support peers through code reviews, design More ❯

data. Collaborate with legal SMEs to translate domain knowledge into scalable machine learning solutions. Continuously evaluate model performance, ensuring accuracy, fairness, and compliance. Help shape the data pipeline and MLOps practices for handling sensitive legal content securely. Required Experience: Solid experience with Python and ML/NLP libraries (e.g., spaCy, Hugging Face, TensorFlow/PyTorch). Experience building NLP or More ❯

independently in a hybrid or remote environment. Experience working in agile environments and iterating based on user feedback. Preferred Skills: Background in deploying AI/ML models to production (MLOps). Experience using cloud platforms such as Azure. Interest or experience in building autonomous agents or internal AI assistants. Why Join? Competitive salary, pension and attractive benefits package. Work on More ❯

AI features, including retrieval-augmented generation (RAG), internal LLM-based tools, and content delivery workflows. Experience with vector stores, agent frameworks, or scalable data workflows is all relevant here. MLOps & Deployment: Build and maintain the infrastructure behind our AI stack – including serving models via APIs, monitoring performance, and deploying systems to the cloud. AWS is a plus, but experience with More ❯

AI features, including retrieval-augmented generation (RAG), internal LLM-based tools, and content delivery workflows. Experience with vector stores, agent frameworks, or scalable data workflows is all relevant here. MLOps & Deployment: Build and maintain the infrastructure behind our AI stack – including serving models via APIs, monitoring performance, and deploying systems to the cloud. AWS is a plus, but experience with More ❯