6 of 6 MLOps Jobs in Maidstone

measurement. Collaborative Development: Work closely with marketing strategists, data analysts, data engineers, and product owners to define use cases and deliver scalable solutions. Model Deployment & Monitoring: Deploy models using MLOps practices and tools (e.g., MLflow, Airflow, Docker, cloud platforms) ensuring performance, reliability, and governance compliance. Innovation & Research: Stay current on advancements in AI/ML and proactively bring forward new 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 ❯

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 ❯

Familiarity with AWS and its tools Excellent communication skills and a collaborative mindset Nice to have: Experience with AI/ML infrastructure or supporting data science workflows Exposure to MLOps tooling or machine learning model deployment 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 ❯