51 to 75 of 113 LlamaIndex Jobs

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-agency mindset. You don’t wait ...

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-agency mindset. You don’t wait ...

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-agency mindset. You don’t wait ...

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-agency mindset. You don’t wait ...

experience in AI/ML with experience developing and deploying solutions using LLMs, RAG architectures, and modern AI frameworks and protocols (Strands, FastMCP, LangChain, LlamaIndex, Hugging Face, A2A, MCP).Ability to translate multi-layered business problems into technical solutions with experience working directly with business stakeholders to understand requirements ...

experience in AI/ML with experience developing and deploying solutions using LLMs, RAG architectures, and modern AI frameworks and protocols (Strands, FastMCP, LangChain, LlamaIndex, Hugging Face, A2A, MCP).Ability to translate multi-layered business problems into technical solutions with experience working directly with business stakeholders to understand requirements ...

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, deployment pipelines ...

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, deployment pipelines ...

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, deployment pipelines ...

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, deployment pipelines ...

impact of your work Although not essential, it would be great if you have practical experience with: Experience with Spring AI, LangChain, LangGraph, LlamaIndex, or similar AI application frameworks Familiarity with modern front-end technologies (such as React and Next.js). Python Kubernetes, Docker and CI/CD tools ...

architectures and fine-tuning approaches; Hands-on experience with prompt engineering and LLM pipeline design, including evaluation; Experience with agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent; Experience with vector databases and retrieval-augmented generation (RAG) patterns; Experience deploying and scaling LLM-powered applications using APIs such ...

haves Fintech/trading/market-data domain experience or experience as a trading-platform user. Open-source contributions to AI tooling (LangChain, LlamaIndex, vLLM, DSPy, or similar). Production RAG and vector-database experience; experience with code-generation, transpilation, or developer-experience tooling. Logistics Location: Europe (EU/ ...

quickly while maintaining high engineering standards Preferred Experience OpenAI, Anthropic, Gemini, or 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 ...

hands-on and writing production code Preferred Experience OpenAI, Anthropic, Gemini, or 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 ...

experience building applications on AWS or other public cloud platformsExperience with Generative AI systems, including: RAG (Retrieval-Augmented Generation) architectureAI orchestration frameworks (LangChain, LlamaIndex, etc.)LLM evaluation frameworks and responsible AI practices (guardrails, content filtering)Preferred Qualifications, Capabilities, and SkillsDeep expertise in Java, object-oriented design, and system designExperience with ...

GenAI experience). Hands-on experience with LLMs (OpenAI, Anthropic, Google, or open-weight models like Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical experience with RAG, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Familiarity with agentic architectures, function/ ...

GenAI experience). Hands-on experience with LLMs (OpenAI, Anthropic, Google, or open-weight models like Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical experience with RAG, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Familiarity with agentic architectures, function/ ...

delivering production-grade systems. Hands-on experience with LLMs (OpenAI, Anthropic, Google, or open-weight models like Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical expertise in RAG design, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Experience with agentic architectures ...

methodology. Orchestration: Production, hands-on experience with an agentic framework such as LangGraph, plus familiarity with MCP-based tool orchestration. Exposure to AutoGen, LlamaIndex, or CrewAI is a plus. Production Python engineering: Clean, modular, testable, maintainable code — you care about system reliability as much as model output. Evaluation & observability: Practical ...

methodology. Orchestration : Production, hands-on experience with an agentic framework such as LangGraph, plus familiarity with MCP-based tool orchestration. Exposure to AutoGen, LlamaIndex, or CrewAI is a plus. Production Python engineering : Clean, modular, testable, maintainable code — you care about system reliability as much as model output. Evaluation & observability : Practical ...

production generative AI initiatives. Hands‐on experience with LLMs (OpenAI, Anthropic, Google, or open‐weight models like Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical expertise in RAG design, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Experience with agentic architectures ...

GenAI experience). Hands‐on experience with LLMs (OpenAI, Anthropic, Google, or open‐weight models like Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical experience with RAG, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Familiarity with agentic architectures, function/ ...

management, control flow (branching, loops, retries), and human in the loop checkpoints, using a framework such as LangChain/LangGraph, Amazon Bedrock AgentCore, CrewAI, LlamaIndex, Strands Agents, Google ADK, or OpenAI Agents SDK. Familiarity with MCP (Model Context Protocol) or similar standards for connecting AI models to external tools ...

modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design. Agentic … programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, 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 ...