Oppstar Technology
AI Agent Engineer
Design, build, and optimize domain-specific AI agents (e.g., for semiconductor design/manufacturing and internet products) end-to-end, from architecture and tool/API integration to evaluation and iteration. Core stack: LLMs, RAG pipelines, orchestration frameworks like LangChain/LlamaIndex, and Python.
- Compensation
- Not disclosed
- Location
- Penang
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Strong understanding of agentic systems (planning, reasoning, memory, tool use, reflection)
- Experience designing modular, scalable, maintainable agent architectures for domain-specific applications
- Hands-on experience with commercial and open-source LLMs
- Proficiency in prompt engineering, chaining, and policy design
- Expertise building RAG pipelines: document ingestion, embedding, vector search, context fusion, answer generation
- Ability to optimize retrieval relevance and mitigate hallucination in domain contexts
- Skill at connecting agents to external tools, databases, and enterprise APIs
- Experience with agent orchestration platforms (LangChain, LlamaIndex, Semantic Kernel, or custom)
- Design robust evaluation strategies; data-driven mindset for continuous improvement and A/B testing
- Solid coding skills in Python
Full description
About the role
Design, build, and optimize domain-specific AI agents across multiple areas such as semiconductor design, semiconductor manufacturing, and internet products. Own the end-to-end lifecycle of AI agents, including requirement discovery, agent architecture design, tool integration, and continuous iteration based on user feedback.
Key responsibilities
- Design multi-step reasoning flows, integrating external tools and APIs
- Conduct prompt and policy optimization, and implement evaluation strategies to measure task success, safety, and reliability
- Work with LLM platforms, retrieval-augmented generation (RAG), and orchestration frameworks
- Translate complex business and engineering use cases into practical AI agent solutions
- Collaborate closely with domain experts to encode workflows and best practices into robust, reliable agentic systems
- Connect agents to external tools, databases, and enterprise APIs (e.g., EDA tools in chip design, CRM, internal microservices)
- Implement multi-agent coordination and complex workflow automation
- Design robust evaluation strategies: task success metrics, safety guardrails, reliability benchmarks, and user feedback loops
About you
- Strong understanding of agentic systems, including planning, reasoning, memory, tool use, and reflection mechanisms
- Experience designing modular, scalable, and maintainable agent architectures for domain-specific applications
- Hands-on experience with commercial and open-source LLMs
- Proficiency in prompt engineering, chaining, and policy design to guide agent behavior
- Expertise in building RAG pipelines: document ingestion, embedding, vector search, context fusion, and answer generation
- Ability to optimize retrieval relevance and mitigate hallucination in domain contexts (e.g., semiconductor or internet product data)
- Skilled at connecting agents to external tools, databases, and enterprise APIs
- Experience with agent orchestration platforms such as LangChain, LlamaIndex, Semantic Kernel, or custom frameworks
- Capable of designing robust evaluation strategies and data-driven mindset for continuous agent improvement and A/B testing
- Solid coding skills in Python