101 to 125 of 268 Pinecone Jobs

clients to enable tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize LLM/VLLM inference stacks using vLLM … experience with RAG architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of Graph RAG/knowledge graph solutions using Neo4j, Neptune, TigerGraph ...

clients to enable tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize LLM/VLLM inference stacks using vLLM … experience with RAG architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of Graph RAG/knowledge graph solutions using Neo4j, Neptune, TigerGraph ...

lakehouse architectures. Experience with data integration tools/ETL-ELT frameworks (e.g., Informatica, dbt, Talend, Glue, ADF). Working knowledge of vector databases (e.g., Pinecone, FAISS, pgvector) and RAG architectures. Experience with agent frameworks/protocols (e.g., LangChain, LangGraph, AutoGen, MCP) is highly desirable. Proficiency in SQL and at least ...

communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.Desirable ExperienceAI agents and agentic workflows.Vector databases such as Pinecone, Weaviate, or pgvector.Feature stores including Feast or AWS Feature Store.Docker, Kubernetes, or Amazon ECS.Infrastructure as Code using Terraform or CloudFormation.Big data processing technologies such ...

pipelines and integrating with enterprise systems (REST, Python, SQL, GraphQL, Webhooks etc). Solid understanding of LLMs, Prompt engineering/tuning, Vector DBs (AstraDB, Pinecone, Weaviate etc), RAG pipelines (LlamaIndex, Haystack) and Agent/Workflow Orchestration (LangChain, LangGraph, CrewAI) Knowledge of SLM Finetuning and Distillation is big advantage. Experience ...

powered applications in enterprise SaaS or complex data platforms. AI & Engineering Mastery: Deep technical expertise in Python or Go, semantic search, vector databases (e.g., Pinecone, Milvus, pgvector), orchestration frameworks (LangChain, LlamaIndex), and fine-tuning or prompt engineering of state-of-the-art Large Language Models (LLMs). Extreme Ownership: High ...

open-source AI 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 ...

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 ...

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 ...

open-source AI 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 ...

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 ...

libraries: NumPy, Pandas, Scikit-learn, XGBoost, spaCy, Hugging Face Transformers, NLTK, Sentence-Transformers, LangChain, LlamaIndex, OpenAI SDK, LangGraph, AutoGen, CrewAI, Oracle, MySQL, SQLAlchemy, FAISS, Pinecone, Weaviate, Qdrant, MLflow, Weights & Biases, BentoML, Airflow. Recruitment Transparency Notice Eliassen Group values transparency in our recruitment practices. Please be advised that Eliassen Group utilizes ...

AutoGen, n8n, or similar tools for building multi-step, multi-agent AI workflows. Working knowledge of retrieval-augmented generation (RAG) patterns, vector databases (e.g., Pinecone, ChromaDB, Weaviate, pgvector), and embedding models. Experience with LLM APIs and platforms including OpenAI, Anthropic Claude, Google Gemini, and/or open-source models ...

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 such ...

securely, and consistently 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 … Semantic Kernel, CrewAI, 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 ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...

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 and DevOps ...