AI Algorithm Engineer

Intel Intel · Semiconductors · California, Santa Clara, United States +2

AI Algorithm Engineer role focused on designing, building, and integrating generative AI agents and platforms using local and cloud-based LLMs. The role involves developing AI-powered products, focusing on agent orchestration, tool usage, RAG, and system optimization, and translating AI logic into production-quality software.

What you'd actually do

  1. Designs and builds generative AI agents and AI platforms leveraging both local and cloud-based large language models (LLMs). Develops AI-powered products and solutions that enable intelligent reasoning, automation, and human-AI interaction for real-world use cases.
  2. Builds and integrates GenAI systems using local LLMs and cloud-hosted LLM services, focusing on agent orchestration, tool usage, retrieval-augmented workflows, and system-level optimization. Collaborates closely with users and stakeholders to define requirements and deliver impactful AI solutions.
  3. Translates AI agent logic, LLM workflows, and data pipelines into production-quality software using modern programming practices. Responsible for end-to-end development, including implementation, testing, debugging, documentation, and deployment of AI services, tools, and platforms.

Skills

Required

  • Bachelor's degree in Computer Science, Electrical/Computer Engineering, or a related field.
  • C++
  • Python

Nice to have

  • LangChain
  • LlamaIndex
  • AutoGen
  • Hugging Face
  • Retrieval-Augmented Generation (RAG)
  • vector databases
  • embedding-based search
  • prompt engineering
  • UX/UI considerations
  • deploying, scaling, and optimizing AI agent platforms
  • building simple AI agents, chatbots, or LLM-based applications
  • AI agents
  • GenAI platforms
  • real-world AI applications
  • Strong problem-solving skills
  • willingness to learn new technologies
  • Good communication skills
  • ability to work in a team environment

What the JD emphasized

  • local and cloud-based large language models (LLMs)
  • agent orchestration
  • tool usage
  • retrieval-augmented workflows
  • production-quality software

Other signals

  • Designs and builds generative AI agents and AI platforms
  • Builds and integrates GenAI systems
  • Translates AI agent logic, LLM workflows, and data pipelines into production-quality software