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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
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.