26 to 50 of 51 Weaviate Jobs

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 with More ❯

a client-facing, consultative role. Deep knowledge of microservices, distributed systems, and event-driven architectures. Strong foundation in AI/ML, especially LLMs, RAG , and vector databases (e.g. Pinecone, Weaviate). Experience with enterprise data integration (e.g. PostgreSQL, SharePoint, APIs, warehouses). Cloud architecture experience (AWS, Azure, or GCP). Proficiency in Python (strongly preferred), and familiarity with Docker and 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 ❯

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 ❯

measurable influenced ARR aligned with GTM metrics. Required Qualifications 5+ years in AI/ML engineering, applied AI, or solutions engineering roles. Proven experience with: RAG pipelines (LangChain, LlamaIndex, Weaviate, Pinecone, Milvus, etc.) Fine-tuning LLMs (LoRA, PEFT, instruction-tuning, domain adaptation) Deploying inference endpoints at scale (Triton, HuggingFace Inference, Ray Serve, Kubernetes) Strong coding skills in Python (and ideally 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 ❯

What we’re looking for: Strong background in software engineering (Python, Java, or similar). Hands-on experience with AI/ML frameworks and tools (LangChain, Hugging Face, LlamaIndex, Weaviate, etc.). Experience deploying AI systems in production environments. Excellent systems thinking, problem-solving, and collaboration skills. This is a chance to shape the future of AI in one of More ❯

and 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 More ❯

and 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 More ❯

Java, Go, or similar. Experience working with Generative AI and ML infrastructure, including: Fine-tuning transformer models Managing embeddings Building RAG pipelines Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search techniques. Strong foundation in cloud infrastructure, networking, security, CI/CD pipelines, and observability. Experience with cloud platforms (AWS, GCP, etc.) or database internals. Proven track record More ❯

Java, Go, or similar. Experience working with Generative AI and ML infrastructure, including: Fine-tuning transformer models Managing embeddings Building RAG pipelines Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search techniques. Strong foundation in cloud infrastructure, networking, security, CI/CD pipelines, and observability. Experience with cloud platforms (AWS, GCP, etc.) or database internals. Proven track record More ❯

Java, Go, or similar. Experience working with Generative AI and ML infrastructure, including: Fine-tuning transformer models Managing embeddings Building RAG pipelines Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search techniques. Strong foundation in cloud infrastructure, networking, security, CI/CD pipelines, and observability. Experience with cloud platforms (AWS, GCP, etc.) or database internals. Proven track record More ❯

Java, Go, or similar. Experience working with Generative AI and ML infrastructure, including: Fine-tuning transformer models Managing embeddings Building RAG pipelines Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search techniques. Strong foundation in cloud infrastructure, networking, security, CI/CD pipelines, and observability. Experience with cloud platforms (AWS, GCP, etc.) or database internals. Proven track record More ❯

Java, Go, or similar. Experience working with Generative AI and ML infrastructure, including: Fine-tuning transformer models Managing embeddings Building RAG pipelines Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search techniques. Strong foundation in cloud infrastructure, networking, security, CI/CD pipelines, and observability. Experience with cloud platforms (AWS, GCP, etc.) or database internals. Proven track record More ❯

years in data engineering, ML engineering, or similar technical roles Strong Python skills and comfort working across complex ingestion workflows Experience managing NoSQL and vector databases at scale (MongoDB, Weaviate, Pinecone, etc.) Solid understanding of modern data pipeline tools (Airflow, Prefect, Dagster) Practical experience with LLM development, embeddings, and RAG architectures Familiarity with distributed systems and cloud platforms (AWS, GCP More ❯