5 of 5 Faiss Jobs in the UK excluding London

consumer behavioural or market data is desirable. Comfortable working within agile engineering teams and enterprise environments. Desirable Skills Experience with vector databases such as FAISS, Pinecone, or Weaviate. Knowledge of enterprise scale Retrieval Augmented Generation systems. Experience within global manufacturing, engineering, or industrial environments. Understanding of data governance, security ...

Python development experience Hands-on experience with Generative AI frameworks (LangChain, LlamaIndex, Haystack) and LLM APIs Knowledge of prompt engineering, vector databases (Pinecone, ChromaDB, FAISS), and model fine-tuning Cloud deployment experience with Docker/Kubernetes Understanding of transformer architectures, AI/ML principles, and MLOps Experience developing REST APIs ...

management, and fine-tuning techniques (LoRA/QLoRA). Experience building RAG pipelines and agentic workflows for enterprise financial applications. Knowledge of vector databases (FAISS, Pinecone, Weaviate) and graph technologies (Neo4j). Familiarity with observability tools and AI evaluation frameworks. Strong grasp of containerization and cloud deployment (Docker, AWS, Azure ...

6+ years of Python development with hands-on experience in Generative AI Strong experience with LLM APIs, prompt engineering, and vector databases (Pinecone, ChromaDB, FAISS) Proven team leadership and mentoring skills ; managing 3-5 engineers Cloud deployment experience with Docker/Kubernetes Deep understanding of transformer architectures, AI/ ...

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 ...