26 to 46 of 46 Weaviate Jobs

ideally in a regulated environment. Preferred qualifications Background in financial services or another regulated enterprise environment. Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such ...

LangChain, Semantic Kernel, or AutoGen. Hands-on experience with Model Context Protocol (MCP) implementations. Strong understanding of vector databases and technologies such as Pinecone, Weaviate, FAISS, or Azure AI Search. Experience building and supporting Retrieval-Augmented Generation (RAG) solutions. Experience working with OpenAI and other modern AI platforms. Expertise ...

Engineer or Senior DeveloperHas breadth of experience across different tech-stacks, such as JavaScript/TypeScript, Java, C#, or Python, amongst othersExperienced in RAG Weaviate, OpenAI, Agents, MCP, Langchain, Prompt Engineering, Browser-Use, and DjangoExperienced building AI Tools, Agents and Agentic workflows (LangGraph, CrwAI)Hands-on Product building experience that ...

Computer Science, Artificial Intelligence, Machine Learning, or a strongly related quantitative fieldHands-on experience with cloud-native ML infrastructure platformsKnowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding modelsExperience with model serving frameworks (vLLM, TensorRT, Ray)Experience with A/B testing and experimentation frameworks for AI featuresContributions to open ...

Aurora, Athena or other similar large scale database technologies. Experience working with AI framework such as LangChain/LlamaIndex Experience with Vector databases (Pinecone, Weaviate, FAISS) This is an outline description of the key responsibilities and accountabilities involved in the job. This is not an exhaustive list and the post ...

tools/functions callable by AI agents (tool schemas, function calling, structured outputs). Experience with retrieval-augmented generation (RAG) : vector databases (e.g., Pinecone, Weaviate, pgvector), embeddings, and chunking strategies. Working knowledge of prompt engineering and evaluation techniques for agent reliability, safety, and cost control. Familiarity with orchestrating multi-agent ...

software engineering, solutions engineering, or ML deployment roleProficiency in Python and working knowledge of AI/ML frameworks (e.g. OpenAI, Hugging Face, LangChain, Pinecone, Weaviate, etc.)Familiarity with building LLM applications, RAG systems, or conversational AI workflowsExperience integrating with enterprise systems via APIs or middleware (e.g. Salesforce, ServiceNow, SAP, Oracle ...

development on Azure Experience building and consuming RESTful APIs; understanding of microservice and event-driven architectures Familiarity with vector databases (Azure AI Search, Qdrant, Weaviate) Experience with Azure DevOps or GitHub Actions for CI/CD A mindset that balances delivery speed with reusability - you look for ways to turn ...

operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune or equivalent). Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector). Practical experience implementing RAG architectures or LLM‐integrated data pipelines. Familiarity with semantic web standards — JSON‐LD, RDF, OWL, or SKOS. Strong ...

fine‐tuning, or serving embedding models (e.g. sentence‐transformers, bi‐encoders, cross‐encoders), with practical experience setting up and managing vector databases (e.g. Qdrant, Weaviate, Milvus, pgvector) along with understanding trade‐offs e.g. when to use sparse or dense embeddings (or both) etc. Classical NLP training and evaluating text classifiers ...

with AI tools and prompting. An understanding of point in time data is a strong plus Also desirable: experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar); machine learning experience (PyTorch, TensorFlow, XGBoost/LightGBM) for building, maintaining and extending existing models; Bloomberg API integration to Python; data science ...

/executor/verifier patterns, tool‐use schemas, retry + escalation logic. Build retrieval pipelines: chunking strategy, vector DB choice (Pinecone/pgvector/Weaviate), reranking, freshness handling. Stay current on Anthropic + OpenAI release notes; adjust prompts + schemas when APIs evolve. We treat the foundation‐model contract ...

building and operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, AmazonNeptune, or equivalent)Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector)Practical experience implementing RAG architectures or LLM-integrated data pipelinesFamiliarity with semantic web standards — JSON-LD, RDF, OWL, or SKOSStrong Python skills ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

tooling (OpenAI/Anthropic/Llama, Hugging Face, LangChain/LlamaIndex, function and tool calling). Retrieval and vector databases (FAISS, pgvector, Pinecone, Weaviate, or similar), and hybrid retrieval that combines graph and vector. Cloud and containers (AWS/GCP/Azure), Docker/Kubernetes, IaC (Terraform/CloudFormation … model (graph, document, key-value), AQL, graph algorithms, GraphRAG Models & SDKs:OpenAI, Anthropic, Meta Llama, Hugging Face Retrieval:graph traversal plus FAISS, pgvector, Pinecone, Weaviate; rerankers (ColBERT, cross-encoders) Pipelines & Orchestration:LangChain, LlamaIndex, Ray, Airflow MLOps & Evals:MLflow, Weights & Biases, Ragas, promptfoo, Great Expectations Serving & Infra:vLLM, TGI, FastAPI/ ...

Demonstrable right to work in the United Kingdom. Desirable Experience designing and deploying RAG (Retrieval‐Augmented Generation) architectures and working with vector databases (Pinecone, Weaviate, Chroma, or similar); Familiarity with enterprise SaaS ecosystems: CRM (Salesforce, HubSpot), ERP, HRIS, marketing platforms, and their API surfaces; Prior experience in a consulting, professional ...

across client use cases Work across LangGraph, LangChain, Claude Code/Codex/Gemini and RAG pipelines Implement RAG and vector database solutions (Pinecone, Weaviate, LlamaIndex) Deploy across cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio Bridge CX and engineering Hold the full stack from CX design … Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector Cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio Leadership & consulting skills Clear communication; able to explain technical decisions ...