1 to 25 of 27 Faiss Jobs in the UK excluding London

Strong stakeholder management and project delivery experience across cross-functional teams. Preferred Qualifications Background in AI ethics, fairness, compliance, or regulatory frameworks. Familiarity with Salesforce, vector databases (Pinecone, Weaviate, FAISS), graph-based reasoning, or knowledge graphs. Experience in property maintenance, home services, or customer service automation (not required, but a bonus). Why Join Us? Be a key player in More ❯

Strong stakeholder management and project delivery experience across cross-functional teams. Preferred Qualifications Background in AI ethics, fairness, compliance, or regulatory frameworks. Familiarity with Salesforce, vector databases (Pinecone, Weaviate, FAISS), graph-based reasoning, or knowledge graphs. Experience in property maintenance, home services, or customer service automation (not required, but a bonus). Why Join Us? Be a key player in More ❯

Strong stakeholder management and project delivery experience across cross-functional teams. Preferred Qualifications Background in AI ethics, fairness, compliance, or regulatory frameworks. Familiarity with Salesforce, vector databases (Pinecone, Weaviate, FAISS), graph-based reasoning, or knowledge graphs. Experience in property maintenance, home services, or customer service automation (not required, but a bonus). Why Join Us? Be a key player in More ❯

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

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

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

Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi-agent systems) ● Experience with vector databases (Pinecone, Weaviate, FAISS) and major cloud platforms (AWS, Azure, GCP) ● Strong foundation in MLOps/LLMOps and Agile delivery ● Excellent leadership, communication, and stakeholder engagement skills ● 10+ years’ experience in AI/ML More ❯

Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi-agent systems) ● Experience with vector databases (Pinecone, Weaviate, FAISS) and major cloud platforms (AWS, Azure, GCP) ● Strong foundation in MLOps/LLMOps and Agile delivery ● Excellent leadership, communication, and stakeholder engagement skills ● 10+ years’ experience in AI/ML More ❯

Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi-agent systems) ● Experience with vector databases (Pinecone, Weaviate, FAISS) and major cloud platforms (AWS, Azure, GCP) ● Strong foundation in MLOps/LLMOps and Agile delivery ● Excellent leadership, communication, and stakeholder engagement skills ● 10+ years’ experience in AI/ML 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). Solid More ❯

a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). Solid More ❯

a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). Solid More ❯

full-stack and AI development. Experience: 5+ years of full-stack development experience. Strong proficiency in Python (FastAPI) and React/Next.js. Experience with RAG systems, vector databases (pgvector, FAISS, Weaviate), and hybrid search. Deep understanding of chunking, indexing, reranking, and evaluation metrics. Solid experience with SQL and NoSQL databases (Postgres, DynamoDB). Familiarity with AI/ML models and More ❯

working with APIs, data structures, and logic flows Good understanding of machine learning concepts , LLMs , tokenisation , and vector-based semantic search Experience working with vector databases (e.g. Chroma, Weaviate, FAISS), and familiarity with relational or graph stores Hands-on experience using frameworks like LangChain , Haystack , or custom orchestration layers Ability to work independently and collaboratively across delivery and engineering teams More ❯

working with APIs, data structures, and logic flows Good understanding of machine learning concepts , LLMs , tokenisation , and vector-based semantic search Experience working with vector databases (e.g. Chroma, Weaviate, FAISS), and familiarity with relational or graph stores Hands-on experience using frameworks like LangChain , Haystack , or custom orchestration layers Ability to work independently and collaboratively across delivery and engineering teams More ❯

working with APIs, data structures, and logic flows Good understanding of machine learning concepts , LLMs , tokenisation , and vector-based semantic search Experience working with vector databases (e.g. Chroma, Weaviate, FAISS), and familiarity with relational or graph stores Hands-on experience using frameworks like LangChain , Haystack , or custom orchestration layers Ability to work independently and collaboratively across delivery and engineering teams More ❯

with zero external dependencies. Key Responsibilities - Build end-to-end RAG pipelines on isolated defence networks using open-source LLMs (Llama 3, Mistral, Qwen) - Deploy local vector stores (Chroma, FAISS, Milvus) with sensitive document ingestion pipelines - Host and optimise LLMs using vLLM/TGI on local GPU clusters without internet connectivity - Implement agent orchestration using LangChain/LangGraph in completely … Requirements - Active SC Clearance (non-negotiable) - willingness to undergo DV if required - Demonstrable experience deploying open-source LLMs (Llama, Mistral, Falcon) on-premises - Expertise with local vector databases (Chroma, FAISS, Weaviate) in offline deployments - Strong vLLM/Text Generation Inference experience for high-throughput model serving - Proven ability to work on air-gapped systems with no external package repositories - Experience … H100) and CUDA optimisation - Python expertise with offline dependency management and local package mirrors Technical Stack (All On-Premises) Models: Llama 3, Mistral, Qwen (locally hosted) Vector Stores: Chroma, FAISS, Milvus Orchestration: LangChain, LangGraph for agents Hosting: vLLM, TGI, Ollama on bare metal/private cloud Infrastructure: Air-gapped Kubernetes, local container registries Desirable Skills - Experience with defence/government More ❯