13 of 13 Permanent Weaviate Jobs

Azure, or GCP) and containerization (Docker, Kubernetes). Knowledge of MLOps/LLMOps practices , including CI/CD pipelines, monitoring, logging, and autoscaling. Understanding of vector databases (e.g. Pinecone, Weaviate, Milvus) and RAG architectures . Strong grasp of system design and ability to translate business needs into scalable technical solutions. Commitment to security, reliability, and performance in production AI systems. More ❯

Microservices architecture. Experience with LLMs and related technologies. Hands-on experience integrating with OpenAI (RAG) services (e.g., GPT models) and Azure AI services. Familiarity with vector databases (e.g., Pinecone, Weaviate, Chroma Experience with version control (Git) and testing. Excellent problem-solving and communication skills. Preferred Qualifications: Experience with cloud platforms (e.g., Azure, AWS, GCP Knowledge of machine learning algorithms and More ❯

first design principles. AI/ML Acumen: Strong foundational knowledge of modern AI concepts, including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and vector databases (e.g., Qdrant, Pinecone, Weaviate). Data Integration: Hands-on experience designing data pipelines and integrating with diverse enterprise data sources (e.g., relational databases like PostgreSQL, unstructured document stores like SharePoint, APIs, and data warehouses More ❯

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 you More ❯

development Hands-on experience with Google Cloud AI services (Vertex AI, Gemini, PaLM) Strong understanding of prompt engineering and LLM optimization techniques Data & Storage: Proficiency with vector databases (Pinecone, Weaviate, Chroma, or similar) Experience with traditional databases (PostgreSQL, MongoDB) Knowledge of data ingestion frameworks and ETL pipelines Understanding of embedding models and semantic search optimization Python Development Advanced Python programming More ❯

platform, with a focus on leveraging AI/GAI technologies and large language models (LLMs) Advanced AI Integration : Apply experience with retrieval-augmented generation (RAG), vector databases (e.g. Pinecone, Weaviate, FAISS), and enterprise search for AI-driven knowledge discovery Optimize AI Performance : Utilize practical experience designing structured prompts, fine-tuning models, and cost optimization strategies Cloud Architecture : Incorporate cloud architecture More ❯

scripts for infrastructure automation. Experience with CI/CD pipelines, configuration management, and onboarding/monitoring of ML systems. Experience working with knowledge graphs and vector databases (e.g., Neo4j, Weaviate, Pinecone, FAISS Strong understanding of data and AI pipeline configuration and orchestration. Equal Opportunity Employer. We are an equal opportunity employer. All aspects of employment including the decision to hire More ❯

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-truth sets 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 ❯

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-truth sets More ❯

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 ❯

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 ❯

integrating with libraries such as Sentence Transformers for embeddings, FAISS for vector search, or Streamlit/Gradio for prototyping interfaces. Experience with vector databases and semantic search (e.g., Pinecone, Weaviate, or FAISS) to support efficient retrieval in LLM applications. Demonstrated expertise in RAG systems, from building retrieval components to integrating them with LLMs for enhanced reasoning and factuality. Proven track … systems, Haystack for RAG pipelines, or evaluation frameworks like Rouge/BLEU for assessing outputs. Familiarity with data-intensive applications, including processing unstructured text, integrating with databases (e.g., Pinecone, Weaviate), or handling multimodal inputs like images for OCR-enhanced workflows. Experience with other cloud platforms (e.g., AWS SageMaker, Azure ML) alongside GCP, including containerization with Docker and orchestration via Kubernetes. More ❯