13 of 13 Faiss Jobs in the UK

integrations). Exposure to AI ethics, data privacy, and compliance regulations. Prior experience in multi-agent systems or autonomous AI workflows. Hands-on experience with vector databases (Pinecone, Weaviate, FAISS) and AI embeddings. Remote WorkingSome remote working CountryUnited Kingdom LocationWC1 Job TypeContract or Permanent Start DateApr-Jul 25 Duration9 months initial or permanent Visa RequirementApplicants must be eligible to work 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 ❯

Biases, and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases (FAISS, Pinecone, ChromaDB). Hands-on with Linux, Bash/Powershell scripting, cloud environments. Creative problem-solver with excellent communication and collaboration skills. Curious, adaptable, and passionate about staying at the More ❯

LLMs. Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and RAG Strong proficiency in Python and experience with frameworks like PyTorch or TensorFlow. Experience working with vector databases (e.g., FAISS, Weaviate, Pinecone). Familiarity with HR systems and data (Workday, SAP SuccessFactors, etc.) is a plus. Excellent communication skills and ability to translate technical concepts for non-technical stakeholders. Ideally More ❯

LLMs. Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and RAG Strong proficiency in Python and experience with frameworks like PyTorch or TensorFlow. Experience working with vector databases (e.g., FAISS, Weaviate, Pinecone). Familiarity with HR systems and data (Workday, SAP SuccessFactors, etc.) is a plus. Excellent communication skills and ability to translate technical concepts for non-technical stakeholders. Ideally 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 ❯

. Design and implement scalable data pipelines using Python, spaCy, Pandas, and Hugging Face Transformers. Build or enhance retrieval-augmented generation (RAG) systems using LangChain and vector databases like FAISS, Weaviate, or Pinecone. Package and deploy solutions via Docker, Kubernetes, or Vertex AI/SageMaker. Collaborate with internal MLOps and Data Science teams to ensure robustness, monitoring, and CI/ 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 ❯

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 ❯

with memory frameworks like Mem0 or Letta. Understanding and production experience with traditional RAG and agentic RAG architectures. Strong grasp of embedding systems and vector databases (e.g., Pinecone, Weaviate, FAISS). Production experience with LiveKit or similar audio/video platforms. What You’ll Be Doing: Building and deploying scalable AI features, agents, and services powered by LLMs. Prototyping and 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 ❯