Senior Genai Software Architect

Intel Intel · Semiconductors · Shanghai, China +1

Senior GenAI Software Architect role focused on building and architecting machine learning products and solutions, with a strong emphasis on GenAI algorithms, LLM-based systems, and AI agent development. The role involves translating ML models into software, optimizing for edge devices, and supporting customer/partner deployments.

What you'd actually do

  1. Builds machinelearning based products/solutions which provide descriptive, diagnostic, predictive, or prescriptive models based on data.
  2. Uses or develops machinelearning algorithms such as supervised and unsupervised learning, deep learning, reinforcement learning, Bayesian analysis, and/or others to solve applied problems in various disciplines such as data analytics, computer vision, and robotics.
  3. Interacts with users to define requirements for breakthrough product/solutions.
  4. In either research environments or specific product environments, utilizes current programming methodologies to translate machine learning models and dataprocessing methods into software.
  5. Completes programming, testing, debugging, documentation and/or deployment of libraries.

Skills

Required

  • Python
  • LangChain
  • LlamaIndex
  • AutoGen
  • HuggingFace
  • RAG pipelines
  • vector databases
  • embedding techniques
  • GenAI algorithms
  • solution architecture
  • performance tuning
  • NLP
  • LLM-based systems
  • AI agent development
  • local LLMs
  • fine-tuning open-source models

Nice to have

  • optimizing GenAI workloads for edge devices using xPU accelerators
  • customer/partner support for GenAI workflow design and deployment
  • client AI tools
  • cross-platform agents
  • plugin ecosystems
  • UX/UI
  • prompt engineering

What the JD emphasized

  • 8+ years hands-on experience on AI/ML algorithm development
  • 3+ years of hands-on experience in NLP, LLM-based systems, or AI agent development
  • Deep expertise in GenAI algorithms, solution architecture, and performance tuning
  • Proven experience building custom AI tools, agents, or apps for real-world use cases

Other signals

  • Builds machine learning based products/solutions
  • Uses or develops machine learning algorithms
  • Translates machine learning models and data processing methods into software
  • Proven experience building custom AI tools, agents, or apps for real-world use cases