Data Science Student for AI Solutions Group

Intel Intel · Semiconductors · Petah-Tikva, Israel +1

Student/Intern role in Intel's AI Solutions Group focused on building agentic systems using LLMs, retrieval, and tool use. Involves hands-on experience with AI agents, RAG pipelines, and LLM orchestration, utilizing frameworks like LangGraph and LangChain. Requires Python programming and foundational ML/DL knowledge.

What you'd actually do

  1. We build agentic systems that reason, plan, and act — combining LLMs, retrieval, and tool use to solve challenging, high-value problems across text, structured data, and multi-step workflows.
  2. We work hands-on with AI agents, RAG pipelines, and LLM orchestration, and we build on frameworks like LangGraph, LangChain, and the broader Python AI ecosystem.
  3. Our AI Engineers are at the core of any new solution we develop, and they're involved in all aspects of the project: from ideation and problem formulation, through designing and prototyping agentic architectures, to preparing solutions for production deployment.

Skills

Required

  • MSc/PhD student focusing on AI, computer science, engineering, or a related field
  • Hands-on experience building with LLMs — e.g. prompting, fine-tuning, evaluation, or working with LLM APIs
  • Practical experience with agentic frameworks and concepts: agent orchestration, tool use, multi-step planning, or frameworks such as LangGraph or LangChain
  • Experience with or strong understanding of RAG (Retrieval-Augmented Generation) — embeddings, vector stores, retrieval pipelines
  • Solid foundational ML/DL knowledge
  • Substantial experience programming in Python, including building and integrating multi-component systems (not just notebooks/scripts)
  • Highly motivated to solve real-world problems and create high impact in practice
  • Team player with great communication skills, who can also work independently and methodically
  • Capacity to work at least 2.5 days a week, with studies expected to continue for at least 1.5 years

Nice to have

  • thesis in a relevant area is a plus

What the JD emphasized

  • Hands-on experience building with LLMs
  • Practical experience with agentic frameworks and concepts
  • Experience with or strong understanding of RAG
  • Substantial experience programming in Python

Other signals

  • building agentic systems
  • LLMs, retrieval, and tool use
  • LangGraph, LangChain
  • Python AI ecosystem