Senior Genai Software Solutions Engineer

Intel · Semiconductors · Penang, Malaysia

Senior GenAI Software Solutions Engineer role focused on architecting, building, and optimizing hybrid AI agents that run across device and cloud environments. Responsibilities include MCP service integration, agentic routing and planning, model runtime engineering, security and compliance, and optimization techniques like quantization and distillation. Requires 5+ years in AI/ML algorithm development and 2+ years in NLP, LLM-based systems, or AI agent development.

What you'd actually do

  1. Architect, build, and optimize AI agents that run seamlessly across device and cloud environments.
  2. Leverage and extend MCP services to enable flexible orchestration, tool integration, and agent coordination.
  3. Implement routing logic and reasoning strategies to improve decision-making and planning across multi-agent and multi-model systems.
  4. Work with different model runtimes, frameworks, and backends to maximize performance.
  5. Ensure model safety, sandboxing, data governance, and secure execution across device and cloud.

Skills

Required

  • Python
  • C++
  • AI/ML algorithm development
  • NLP
  • LLM-based systems
  • AI agent development
  • GenAI algorithms
  • solution architecture
  • performance tuning
  • building custom AI tools, agents, or apps

Nice to have

  • RAG pipelines
  • vector databases
  • embedding techniques
  • optimizing GenAI workloads for edge devices
  • xPU accelerators
  • local LLMs
  • fine-tuning open-source models
  • customer/partner support for GenAI workflow design and deployment
  • LangChain
  • LlamaIndex
  • AutoGen
  • HuggingFace
  • APIs
  • UX/UI
  • prompt engineering

What the JD emphasized

  • hands-on with AI systems engineering
  • integrating multiple models and runtimes
  • building secure, scalable, and efficient AI solutions
  • next-generation agentic applications
  • AI/ML algorithm development
  • NLP, LLM-based systems, or AI agent development
  • GenAI algorithms, solution architecture, and performance tuning
  • building custom AI tools, agents, or apps for real-world use cases

Other signals

  • AI systems engineering
  • integrating multiple models and runtimes
  • building secure, scalable, and efficient AI solutions
  • next-generation agentic applications