AI Scientist - LLM & AI Agent

Responsibilities

• Design and implement modular AI agents using frameworks such as LangGraph , enabling multi-turn reasoning, tool usage, and context retention.
• Build agentic workflows with Model Context Protocol (MCP) to manage dynamic memory, context injection, and action coordination.
• Develop and fine-tune LLMs (e.g., GPT-4, Qwen, LLaMA, Mistral) for downstream tasks such as multi-agent collaboration, structured generation, and reasoning.
• Integrate Retrieval-Augmented Generation (RAG) architectures with vector databases (e.g., FAISS, Milvus) to support context-aware responses and tool routing.
• Engineer prompts and multi-step reasoning chains for LLMs in agent-based environments.
• Deploy and optimize open-source LLMs using vLLM , Triton , or Ollama , ensuring low-latency inference at scale.
• Translate core NLP capabilities into production-ready agent behaviors, collaborating with engineering and product teams.
• Stay at the forefront of agentic AI research , contributing to internal frameworks and incorporating cutting-edge ideas from open source and academia.
• Present project outcomes and model behaviors to both technical and non-technical stakeholders to guide product strategy.

Requirements

• Master's, or PhD in Computer Science , Artificial Intelligence , or a related field.
• 2+ years of experience in AI or NLP-focused roles , including production deployment of LLM-driven systems.
• Proficiency in Python and libraries such as Transformers , LangGraph , LangChain , and PyTorch .
• Strong knowledge of prompt engineering , context management , and multi-agent orchestration .
• Hands-on experience deploying and scaling LLMs in production environments.
• Familiarity with retrieval mechanisms , vector search , and memory architectures in agent workflows.
• Able to use Chines and English as daily working lanague to work with chinese speaking skatesholder

Additional Information

We are looking for highly skilled NLP & LLM Experts to join our AI team and drive innovation in Agentic AI systems , focusing on reasoning, memory management, tool use, and multi-agent orchestration. This role is ideal for individuals who are passionate about building intelligent systems that go beyond traditional LLM applications - leveraging LangGraph , Model Context Protocol (MCP) , Retrieval-Augmented Generation (RAG) , and modular AI agents to power dynamic, context-aware applications. You'll help shape the next generation of intelligent automation across domains such as customer support, internal tooling, and knowledge orchestration.

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Company Intel

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