26 to 50 of 71 Permanent Pinecone Jobs

Foundry or Microsoft Agent Framework Knowledge of MCP and A2A protocols for agent and tool interoperability Hands-on work with vector databases like Pinecone, Weaviate, Qdrant or pgvector Familiarity with distributed systems, workflow engines (Temporal, Airflow or Dagster) and event-driven architectures Experience with open-source LLMs or Small Language ...

dialogue management, prompt engineering, and Retrieval-Augmented Generation (RAG). Data Analytics: Familiarity with real-time data streaming (e.g., Kafka) and vector databases (e.g., Pinecone, Milvus, Qdrant). Quantization: Experience with model compression techniques like quantization (INT8/FP4), pruning, and distillation for edge deployment. #J-18808-Ljbffr ...

appropriately and recognising when non-AI approaches are more suitableDesirable ExperienceAdvanced LLM techniques, including agents, tool use and agentic workflowsExperience with vector databases (e.g. Pinecone, Weaviate, pgvector)Feature store technologies such as Feast or AWS Feature StoreContainerisation and orchestration using Docker, Kubernetes or ECSInfrastructure as Code using Terraform or CloudFormationLarge ...

REST/SDK — for use cases such as document extraction, policy summarisation, or intelligent routing. Build or consume vector-search capabilities (e.g., OpenSearch, Pinecone) for retrieval-augmented generation (RAG) patterns. Use AI coding assistants (Claude Code, GitHub Copilot, Cursor, etc.) fluently as part of your daily development workflow to raise ...

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 ...

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 ...

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 model ...

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

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 ...

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

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

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 ...

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 ...

Science, Artificial Intelligence, Machine Learning, or a strongly related quantitative field Hands‐on experience with cloud‐native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features ...

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 ...

Science, Artificial Intelligence, Machine Learning, or a strongly related quantitative field Hands‐on experience with cloud‐native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features ...