Senior GenAI Software Solutions Engineer

Requirements

• 5+ years hands-on experience on AI/ML algorithm development
• 2+ 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.
• Strong Python or C++ skills.
• Excellent problem-solving skills with a results-driven, customer-focused mindset.
• Familiarity with client AI tools, cross-platform agents, or plugin ecosystems.
• Preferred skills: -
• Experience with RAG pipelines, vector databases (e.g.,FAISS, Chroma), and embedding techniques.
• Experience optimizing GenAI workloads for edge devices using xPU accelerators.
• Experience with local LLMs (e.g., Mistral, Llama) or fine-tuning open-source models.
• Experience in customer/partner support for GenAI workflow design and deployment.
• Experience with frameworks such as LangChain, LlamaIndex, AutoGen, HuggingFace, and other APIs.
• Experience in UX/UI or prompt engineering to improve human-AI interaction.
• Job Type:
• Experienced Hire
• Shift:
• Shift 1 (Malaysia)
• Primary Location:
• Malaysia, Penang
• Additional Locations:
• Business group:
• Posting Statement:
• Position of Trust
• N/A
• Work Model for this Role
• This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.
• *

Benefits

Additional Information

Job Details: Job Description: The ideal candidate is hands-on with AI systems engineering, has experience integrating multiple models and runtimes, and is passionate about building secure, scalable, and efficient AI solutions that power next-generation agentic applications. Responsibilities: - Hybrid AI Agent Development: Architect, build, and optimize AI agents that run seamlessly across device and cloud environments. MCP Service Integration: Leverage and extend MCP services to enable flexible orchestration, tool integration, and agent coordination. Agentic Routing and Planning: Implement routing logic and reasoning strategies to improve decision-making and planning across multi-agent and multi-model systems. Model Runtime Engineering: Work with different model runtimes, frameworks, and backends to maximize performance. Security and Compliance: Ensure model safety, sandboxing, data governance, and secure execution across device and cloud. Optimization: Apply techniques like model quantization, pruning, distillation, and caching for efficiency across diverse environments. Technical Evangelism: Contribute to best practices, design patterns, and technical documentation to support broader adoption of hybrid AI agent architectures.

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