4 of 4 Contract Faiss Jobs in England

Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including process modelling ...

analysis on structured and unstructured datasets using Python, SQL, and big data frameworks (Hadoop, PySpark). Develop and optimize data retrieval systems using RAG, FAISS, Elasticsearch, and other retrieval algorithms. Implement time-series forecasting and anomaly detection for performance monitoring and optimization. Create data visualizations and dashboards using tools like ...

integrating retrieval systems with large language models. Build and maintain applications using LangChain to orchestrate complex AI workflows. Implement and manage vector databases (e.g., FAISS, Pinecone, Weaviate, Milvus) for fast similarity search and retrieval. Develop embedding pipelines for unstructured data using models like Sentence Transformers, OpenAI, or Hugging Face. Collaborate … large language models (GPT, BERT, etc.). Fine-tuning and prompt engineering. Experience with vector similarity search. Knowledge of popular vector DBs: Pinecone, Weaviate, FAISS, Vespa, Milvus. Generating and managing embeddings (using models like SentenceTransformers, OpenAI, Hugging Face). Familiarity with LangChain for building LLM-powered applications. Ability to create ...

evaluation, tuning, and optimization. Work with LLM architectures and frameworks (OpenAI, Anthropic, LangChain, LlamaIndex) to build scalable and intelligent applications. Utilize vector databases (e.g., FAISS, Azure AI Search) and embedding models for semantic search and retrieval tasks. Execute and iterate on PoCs rapidly to evaluate feasibility and solution effectiveness. Collaborate …/Authorization (OAuth2, JWT, Azure AD) NLP techniques (NER, classification, chunking, summarization, QA) RAG architectures and working with LLMs (OpenAI, Anthropic, etc.) Vector databases (FAISS, Azure AI Search ...