24 of 24 Remote Weaviate Jobs

stakeholders. 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 More ❯

stakeholders. 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 More ❯

Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning, Vector Databases, Pinecone, Weaviate, Semantic Search, API Development, AI Deployment, Model Serving, AI Automation, Early Stage Startup, AI Startups, Tech Startup, Machine Intelligence, Applied AI, AI Applications, AI Innovation, AI Product Development, AI Tools More ❯

Llama, 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/ More ❯

Llama, 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/ More ❯

improve response quality and domain-specific performance. Optimize embedding models for better semantic search and document retrieval. Integrate AI-driven solutions with vector databases such as DataStax Astra DB, Weaviate, or FAISS. Implement secure API authentication (OAuth2, JWT) and manage access controls. Collaborate with AI/ML, data engineering, and DevOps teams to ensure seamless system integration. Monitor API performance 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 (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 More ❯

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 understanding More ❯

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 understanding More ❯

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 understanding More ❯

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 understanding More ❯

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 understanding More ❯

RAG), and AI agents) Build and maintain APIs, data pipelines, and backend components (primarily Python, FastAPI, or Flask) Engineer robust, containerised architectures using Docker and Kubernetes Work with ElasticSearch, Weaviate, Pinecone, and other vector databases Help deliver high-availability, fault-tolerant systems for mission-critical workloads What Were Looking For Degree in Computer Science, AI, or related field, or equivalent More ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

have immediate, visible impact. 🔧 What You’ll Be Doing Designing secure, scalable APIs in Python/FastAPI Building and optimising Retrieval-Augmented Generation (RAG) pipelines Owning vector databases (pgvector, Weaviate, FAISS) and embedding strategies Integrating OpenAI/Bedrock models and developing guardrails & evaluation flows Ingesting and structuring data for high-performance retrieval and analytics Building clean, fast UIs with React More ❯