18 of 18 Faiss Jobs in the UK

OpenAI GPT models, Anthropic Claude, 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, interface design, expression rules, integration More ❯

Python skills, with experience usingTransformers,LangChain,LlamaIndex and the broader GenAI ecosystem. Deep understanding of LLM behavior, prompt optimization, embeddings, retrieval and data preparation workflows. Experience with vector DBs (FAISS, Pinecone, Milvus, Weaviate, ChromaDB). Hands-on knowledge of Linux, Bash/Powershell, containers and cloud environments. Strong communication skills, creativity and a systems-thinking mindset. Curiosity, adaptability and a 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 ❯

Experience 2-6 years of 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 Desirable Exposure to Insurtech or More ❯

Experience 2-6 years of 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 Desirable Exposure to Insurtech or More ❯

high-quality outputs. Skills & Experience 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/ML principles, and MLOps Experience developing More ❯

high-quality outputs. Skills & Experience 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/ML principles, and MLOps Experience developing More ❯

and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to the creation of a strategic AI adoption roadmap and reusable AI More ❯

and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to the creation of a strategic AI adoption roadmap and reusable AI More ❯

training datasets (structured/unstructured text, images, code). Deep knowledge of data preprocessing, tokenization, and embedding generation techniques. Hands-on experience working with Vector Databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Stakeholder & Strategic Partnership: Ability to partner effectively with business stakeholders to identify, shape, and prioritize high-impact AI use cases. Experience contributing to a More ❯

training datasets (structured/unstructured text, images, code). Deep knowledge of data preprocessing, tokenization, and embedding generation techniques. Hands-on experience working with Vector Databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Stakeholder & Strategic Partnership: Ability to partner effectively with business stakeholders to identify, shape, and prioritize high-impact AI use cases. Experience contributing to a More ❯

to the data science or ML community (libraries, notebooks, packages, or tutorials). Experience presenting research or applied work at meetups, workshops, or industry conferences. Familiarity with vector databases (FAISS, Pinecone, Milvus, Weaviate) and LLM application frameworks. Cloud or AI/ML certifications (e.g., AWS Machine Learning Specialty, Google Professional Data Engineer, Azure AI Engineer) are a plus. Benefits Work More ❯

value. A pragmatic, delivery-focused mindset — comfortable in fast-moving, evolving environments. The tech environment: Python, PyTorch/TensorFlow, HuggingFace Transformers Semantic search/Embeddings/Vector DBs (e.g., FAISS, Pinecone, Weaviate) ML Ops tools (MLflow/Weights & Biases) AWS/Docker/Kubernetes If you’re the Head of AI & Machine Learning we’re looking for then we want More ❯

value. A pragmatic, delivery-focused mindset — comfortable in fast-moving, evolving environments. The tech environment: Python, PyTorch/TensorFlow, HuggingFace Transformers Semantic search/Embeddings/Vector DBs (e.g., FAISS, Pinecone, Weaviate) ML Ops tools (MLflow/Weights & Biases) AWS/Docker/Kubernetes If you’re the VP of AI & Machine Learning we’re looking for then we want More ❯

tooling (Git, CI/CD, API integration), front-end & visualization (Streamlit, Gradio, React), and cloud environments (AWS/GCP/Azure). Experience with vector and graph databases (Pinecone, FAISS, Neo4j). Understanding of model evaluation metrics and automation techniques. Awareness of AI ethics and responsible AI practices. Bachelor's, Master's, or PhD in Computer Science, Engineering, or a More ❯

GraphRAG or Graph-based RAG architectures. Proficiency in Python and frameworks like LangChain, Semantic Kernel, or similar. Hands-on with graph databases (Neo4j, TigerGraph) and vector DBs (Pinecone, Weaviate, FAISS). Knowledge of LLMs, prompt engineering, and retrieval optimization. Familiarity with knowledge graph construction, entity linking, and graph algorithms. Must have full right to work and live in the UK More ❯

Banking APIs (TrueLayer, Yapily, Salt Edge, etc.). Hands-on experience with AI/LLM-powered systems — especially embedding models (OpenAI, Cohere, Sentence Transformers) and vector databases (Pinecone, Weaviate, FAISS, Chroma). Strong understanding of secure data practices and compliance standards. Excellent communicator — able to translate complex technical ideas to non-technical founders. Calm, methodical, and collaborative — a natural problem More ❯

best practices in AI model 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 with cross-functional teams and … service communication (REST, Kafka) Authentication/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 More ❯