AI Agent Engineer

Oppstar Berhad; Kajang; On-site; MYR 180,000 - 300,000; Full time

Job summary

Oppstar Berhad designs, builds, and optimizes domain-specific AI agents across semiconductor design, manufacturing, and internet products. You will own the end-to-end lifecycle of AI agents, from requirement discovery to architecture, tool integration, and continuous iteration based on user feedback.

Responsibilities include designing multi-step reasoning flows, integrating tools and APIs, and developing robust evaluation strategies for safety, reliability, and task success.

Qualifications

• Experience designing modular, scalable, and maintainable agent architectures for domain-specific applications.
• Experience with commercial and open-source LLMs.
• Proficiency in prompt engineering, chaining, and policy design.

Responsibilities

• Design multi-step reasoning flows, integrating external tools and APIs.
• Conduct prompt and policy optimization, and implement evaluation strategies for task success, safety, and reliability.
• Work with LLM platforms, RAG, and orchestration frameworks.
• Translate complex business and engineering use cases into practical AI agent solutions.
• Collaborate with domain experts to encode workflows and best practices into robust agentic systems.
• Connect agents to external tools, databases, and enterprise APIs.
• Implement multi-agent coordination and complex workflow automation.
• Design evaluation strategies: task success metrics, safety guardrails, reliability benchmarks, and user feedback loops.

Skills

Agent systems design

LLM platforms

Prompt engineering

RAG pipelines

Context fusion

Tool integration

Multi-agent coordination

A/B testing

Tools

LangChain

LlamaIndex

Semantic Kernel

Custom frameworks

Job description

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