126 to 150 of 182 pgvector Jobs

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

Integrate with AWS Bedrock and other LLM platforms to support multi-model capabilities Develop data access and semantic search layers using vector databases (e.g., pgvector, Pinecone, Qdrant) Build robust monitoring, testing, and CI/CD systems to ensure reliability and reproducibility of AI workflows Collaborate closely with the product ...

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 to non-engineers Collaborative ...

practices.Experience and Skills (Nice to have)· Previous experience building a user-facing GenAI/LLM software application· Previous experience with vectors and embeddings (pgvector, chromadb)· Knowledge of LLM RAG/Agent core concepts and fundamentals· Experience with Neo4J, GraphQL for efficient data querying for APIs,· CI/CD tools (e.g. ...

governance practices, 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 ...

platforms and scalable pipelines. Excellent knowledge of PostgreSQL (or similar relational databases) and NoSQL databases. Experience with vector databases such as Qdrant, Milvus or pgvector . Familiarity with technologies such as Apache Spark, Airflow, Kafka, Elasticsearch/OpenSearch, Pandas and Polars . Strong understanding of data modelling, storage architecture ...

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 and experience with ...

/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 workflows ...

Claude, etc.) critically and effectively as part of their day-to-day workflow.Bonus points for:Experience with retrieval-augmented generation (RAG) and vector databases (pgvector, Pinecone, or similar).Experience designing multi-agent systems and agent orchestration patterns.Familiarity with LLM observability tools (LangSmith, Langfuse, Arize, or similar).Experience working with multiple ...

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 CI/CD for AI: version-controlled prompts, automated testing, deployment pipelines ...

etc.) critically and effectively as part of their day-to-day workflow. Bonus points for: Experience with retrieval-augmented generation (RAG) and vector databases (pgvector, Pinecone, or similar).Experience designing multi-agent systems and agent orchestration patterns. Familiarity with LLM observability tools (LangSmith, Langfuse, Arize, or similar).Experience working with ...

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 CI/CD for AI: version‐controlled prompts, automated testing, deployment pipelines ...

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 CI/CD for AI: version-controlled prompts, automated testing, deployment pipelines ...

across teams.• Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination … AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.• Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that ...

models Model Context Protocol and agent orchestration frameworks LangGraph, LlamaIndex, Vercel AI SDK, or similar tools Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector Voice AI, WebSockets, WebRTC, or real-time streaming systems AI evaluations, observability, prompt injection protection, and model security Docker, AWS, Google Cloud, Azure, or serverless ...

models Model Context Protocol and agent orchestration frameworks LangGraph, LlamaIndex, Vercel AI SDK, or similar tools Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector Voice AI, WebSockets, WebRTC, or real-time streaming systems AI evaluations, observability, prompt injection protection, and model security Docker, AWS, Google Cloud, Azure, or serverless ...