1 to 25 of 104 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 ...

libraries (LangChain, PyTorch, TensorFlow, HuggingFace Transformers). Experience with building Q&A systems and retrieval-augmented generation pipelines. Knowledge of vector databases or semantic search concepts. Familiarity with cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI). Knowledge of MLOps practices and deployment pipelines. Ability … Azure Container Apps (ACA), Azure Kubernetes Service (AKS), or serverless functions (Azure Functions) for event-driven agent triggers. Experience implementing RAG using Azure AI Search (vector, semantic, and hybrid search) and OneLake/Microsoft Fabric. Nice to Have Skills Certification: Microsoft Certified: Azure AI Engineer Associate ...

Design RAG systems: chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search, pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings … including document understanding, automatic summarisation, topic modelling, semantic search, entity recognition, and relationship extraction. Design and implement intelligent tagging and metadata enrichment frameworks to categorize and organize legal and market data, improving search, discoverability, and insight accuracy. LLM & Machine Learning Application Engineering Design, build, and maintain traditional ...

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 ...

/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic ...

/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic ...

distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. ...

distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. ...

distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. ...

language models. Proficiency in Python and familiarity with deep learning/NLP libraries (Langchain, PyTorch, TensorFlow, HuggingFace Transformers). Knowledge of vector databases or semantic search concepts is desirable. Eagerness to learn, strong problem-solving skills, and ability to work collaboratively in agile teams. Experience with Git, collaborative ...

language models. Proficiency in Python and familiarity with deep learning/NLP libraries (Langchain, PyTorch, TensorFlow, HuggingFace Transformers). Knowledge of vector databases or semantic search concepts is desirable. Eagerness to learn, strong problem-solving skills, and ability to work collaboratively in agile teams. Experience with Git, collaborative ...

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 ...