1 to 25 of 64 Permanent Semantic Search Jobs in England

production—in a modern, AWS-based environment. You'll be part of an engineering and data team tackling projects like LLM development, intelligent automation, semantic search, and recommendation systems. There's real scope here to influence how MLOps and model deployment are done, and you'll be working … Implementing MLOps practices: versioning, experiment tracking, CI/CD, monitoring Optimising for performance, scalability and cost in the cloud Integrating vector databases, embeddings and semantic search Collaborating with engineering, data and product teams What you'll need: Strong Python and experience with TensorFlow, PyTorch or scikit-learn Hands ...

looking for a founding AI engineer to help design and build the core of its AI platform. AI is central to the product, powering semantic search, intelligent matching, assistants, and personalised experiences across web and mobile. You'll work closely with the founders to design and deliver creative … features that genuinely change how people discover and book services. What you'll be doing Architect and build AI-driven backend services powering semantic search, natural language interfaces, recommendations and assistants Integrate and deploy LLM APIs (OpenAI, Anthropic, Cohere) and open-source AI frameworks (LangChain, LlamaIndex, LangGraph) into ...

looking for a founding AI engineer to help design and build the core of its AI platform. AI is central to the product, powering semantic search, intelligent matching, assistants, and personalised experiences across web and mobile. You’ll work closely with the founders to design and deliver creative … features that genuinely change how people discover and book services. What you’ll be doing Architect and build AI-driven backend services powering semantic search, natural language interfaces, recommendations and assistants Integrate and deploy LLM APIs (OpenAI, Anthropic, Cohere) and open-source AI frameworks (LangChain, LlamaIndex, LangGraph) into ...

looking for a founding AI engineer to help design and build the core of its AI platform. AI is central to the product, powering semantic search, intelligent matching, assistants, and personalised experiences across web and mobile. You’ll work closely with the founders to design and deliver creative … features that genuinely change how people discover and book services. What you’ll be doing Architect and build AI-driven backend services powering semantic search, natural language interfaces, recommendations and assistants Integrate and deploy LLM APIs (OpenAI, Anthropic, Cohere) and open-source AI frameworks (LangChain, LlamaIndex, LangGraph) into ...

Recommendation and ranking services Classification, quality scoring, and enrichment pipelines Implement and evolve the AI/ML platform, including: Feature and embeddings stores Vector search/semantic search infrastructure Evaluation dashboards, prompt/version management, and feedback loops Own services end-to-end: from design and implementation … teams. Hands-on experience with AI/ML or GenAI in production, such as: Fine-tuning or integrating transformer models Building RAG pipelines and semantic search Working with vector databases (e.g., Pinecone, Weaviate, Milvus, OpenSearch, etc.) Self-motivated and proactive, able to work independently where needed while contributing ...

Recommendation and ranking services Classification, quality scoring, and enrichment pipelines Implement and evolve the AI/ML platform, including: Feature and embeddings stores Vector search/semantic search infrastructure Evaluation dashboards, prompt/version management, and feedback loops Own services end-to-end: from design and implementation … teams. Hands-on experience with AI/ML or GenAI in production, such as: Fine-tuning or integrating transformer models Building RAG pipelines and semantic search Working with vector databases (e.g., Pinecone, Weaviate, Milvus, OpenSearch, etc.) Self-motivated and proactive, able to work independently where needed while contributing ...

Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning, Vector Databases, Pinecone, Weaviate, Semantic Search, API Development, AI Deployment, Model Serving, AI Automation, Early Stage Startup, AI Startups, Tech Startup, Machine Intelligence, Applied AI, AI Applications ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...