11 of 11 Permanent Faiss Jobs in England

skills in Python and familiarity with AI frameworks (e.g., PyTorch, TensorFlow, JAX). Experience with MLOps tools such as MLflow, DVC, or Kubeflow. Familiarity with LLMs, vector databases (e.g., FAISS, Weaviate), and RAG pipelines. Understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). More ❯

in Python, PyTorch, TensorFlow, and Hugging Face Transformers. Hands-on experience with LangChain, CrewAI, and LangFlow (bonus points for deep expertise). Strong understanding of vector databases (Pinecone, Weaviate, FAISS) and embedding models. Experience building production-ready AI products, ensuring scalability and reliability. Deep knowledge of prompt engineering, tokenization strategies, and data augmentation for LLMs. Familiarity with ML-Ops best More ❯

similar Experience with relevant technologies such as OpenAI, LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not More ❯

hands-on engineer with an ownership mindset, strong communication skills, and a collaborative approach. 5+ years’ experience in full-stack development. Strong background in RAG systems , vector databases (pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods. Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques. Proficient in Python (FastAPI) and React More ❯

/ownership mindset, strong communication skills and a team player 5+ years of professional experience in full-stack development. Hands-on experience with RAG systems , vector databases (pgvector/FAISS/Weaviate/ES k-NN), embeddings , and hybrid search (BM25 + vectors). Strong grasp of chunking strategies , metadata, indexing, recall/precision trade-offs, reranking, and evaluation (ground More ❯

e.g., GPT, Llama, Claude) GenAI & Agentic frameworks (LangChain, LangGraph, CrewAI) Python & AI/ML libraries (Transformers, spaCy, etc.) RAG, Agentic RAG, and multi-agent systems Vector databases (Pinecone, Weaviate, FAISS) If this aligns with your background, I’d love to share more or set up a quick call. Best regards, Shikha Yadav More ❯

e.g., GPT, Llama, Claude) GenAI & Agentic frameworks (LangChain, LangGraph, CrewAI) Python & AI/ML libraries (Transformers, spaCy, etc.) RAG, Agentic RAG, and multi-agent systems Vector databases (Pinecone, Weaviate, FAISS) If this aligns with your background, I’d love to share more or set up a quick call. Best regards, Shikha Yadav More ❯

e.g., GPT, Llama, Claude) GenAI & Agentic frameworks (LangChain, LangGraph, CrewAI) Python & AI/ML libraries (Transformers, spaCy, etc.) RAG, Agentic RAG, and multi-agent systems Vector databases (Pinecone, Weaviate, FAISS) If this aligns with your background, I’d love to share more or set up a quick call. Best regards, Shikha Yadav More ❯

delivered to production, ideally involving document understanding, NLP or search/retrieval systems · Practical knowledge and hands-on experience with: RAG architectures, LLMs (e.g., OpenAI, Antropic), Vector databases (e.g., FAISS, Azure AI Search), Embeddings (e.g., OpenAI) · Strong grasp of NLP techniques: named entity recognition (NER), document classification, chunking, summarization, question answering. · Ability to evaluate trade-offs and select appropriate ML More ❯

delivered to production, ideally involving document understanding, NLP or search/retrieval systems · Practical knowledge and hands-on experience with: RAG architectures, LLMs (e.g., OpenAI, Antropic), Vector databases (e.g., FAISS, Azure AI Search), Embeddings (e.g., OpenAI) · Strong grasp of NLP techniques: named entity recognition (NER), document classification, chunking, summarization, question answering. · Ability to evaluate trade-offs and select appropriate ML More ❯

LLMs and prompt design (ideally OpenAI or similar) An understanding of structured data (SQL, JSON, CSV) and unstructured text (PDFs Exposure to RAG architectures and vector stores (such as FAISS, Pinecone...) Prompt Engineer (AI & Data) – Could suit a Graduate/Junior or Experienced professional Location: Remote (M4/M5/M50 area preferred but flexible) Salary: £30-60k (depending … design (ideally OpenAI or similar) An understanding of structured data (SQL, JSON, CSV) and unstructured text (PDFs, documents, form data) Exposure to RAG architectures and vector stores (such as FAISS, Pinecone.....) The ability to work with and communicate with multi-disciplinary teams at all levels of an organization Any experience in a professional services/regulated industry or knowledge of More ❯