Senior AI Engineer
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What you'll do
- Design and build the company-wide AI knowledge infrastructure, including company wiki, internal knowledge base, retrieval layer, and context management system.
- Develop scalable LLM application architecture, including RAG pipelines, vector database integration, prompt workflows, API services, monitoring, and deployment.
- Own the end-to-end technical delivery of internal AI tools, from backend architecture and basic frontend integration to deployment, testing, and monitoring.
- Work closely with business, brand, PR, IR, and leadership stakeholders to translate ambiguous business needs into practical AI systems and technical roadmaps.
- Optimize system performance, including token efficiency, latency, caching strategy, retrieval quality, data architecture, and model inference flow.
- Evaluate and integrate AI coding tools, LLM frameworks, vector databases, and third-party APIs to improve development efficiency and product quality.
- Mentor junior engineers or interns when needed, and help establish technical standards, documentation practices, and reusable engineering workflows.
What they're looking for
- 4–7 years of backend engineering experience, with at least 2 years of hands-on LLM application development experience.
- Strong backend development skills in Python; experience with Node.js or Go is a plus.
- Solid computer science fundamentals, including algorithms, system design, database design, API architecture, distributed systems, caching, and performance optimization.
- Production-level LLM application experience, not limited to demos or prototypes. Experience should include prompt engineering at scale, model selection, inference pipeline design, or RAG architecture.
- Hands-on experience with RAG and vector databases such as Pinecone, Weaviate, Chroma, or similar tools.
- Experience owning full engineering delivery, including backend services, basic frontend integration, API deployment, monitoring, and troubleshooting.
- Heavy user of AI coding tools such as Cursor, Claude Code, GitHub Copilot, or similar tools.
- Mandarin fluency is required; English working proficiency is required.
- Able to work independently under ambiguous instructions and make sound technical decisions without waiting for detailed specifications.
Nice to have
- experience with Node.js or Go is a plus.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Senior AI Engineer
We’re looking for a Senior AI Engineer to help build the technical foundation for Patsnap’s next phase of AI transformation. You’ll design and develop the knowledge and context infrastructure that connects our internal information and powers a new generation of intelligent tools and AI-driven workflows across the business.
This is a highly hands-on, high-ownership role spanning LLM application architecture, RAG, retrieval, knowledge systems, context management, and full-stack AI development. You’ll work directly with teams and senior stakeholders across Patsnap to turn complex and sometimes ambiguous needs into practical AI solutions—taking them from initial idea and architecture through to deployment and continuous improvement. Beyond building individual applications, you’ll establish reusable infrastructure, engineering standards, and technical patterns that make it faster and easier to build reliable AI solutions across the company.
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Check out this short overview:
This is an in-office position based in our Singapore office.
Who are we?
Patsnap is a global, pre-IPO company that transforms the way organizations harness their Intellectual Property and Research & Development productivity. Our platform revolutionizes how IP and R&D teams collaborate across the entire innovation lifecycle, using domain-specific AI to accelerate the creation of market-ready products. With over 12,000 customers worldwide, including some of the biggest names in innovation, Patsnap is at the forefront of technological advancement. Our $300M Series E funding round brings our valuation to a $1 billion unicorn status, and we still have a remarkable amount of growth ahead.
We have a vibrant and diverse team with offices in Singapore, Toronto, London, Shanghai and remote teams based in US. Our hyper-growth trajectory is powered by our people, and we are extremely proud of our company-wide vision, work ethic, and entrepreneurial spirit. We are committed to fostering an inclusive environment where talent thrives and ideas bloom.
What You'll Be Doing:
Design and build the company-wide AI knowledge infrastructure, including company wiki, internal knowledge base, retrieval layer, and context management system.
Develop scalable LLM application architecture, including RAG pipelines, vector database integration, prompt workflows, API services, monitoring, and deployment.
Own the end-to-end technical delivery of internal AI tools, from backend architecture and basic frontend integration to deployment, testing, and monitoring.
Work closely with business, brand, PR, IR, and leadership stakeholders to translate ambiguous business needs into practical AI systems and technical roadmaps.
Optimize system performance, including token efficiency, latency, caching strategy, retrieval quality, data architecture, and model inference flow.
Evaluate and integrate AI coding tools, LLM frameworks, vector databases, and third-party APIs to improve development efficiency and product quality.
Mentor junior engineers or interns when needed, and help establish technical standards, documentation practices, and reusable engineering workflows.
What We'd Love From You:
4–7 years of backend engineering experience, with at least 2 years of hands-on LLM application development experience.
Strong backend development skills in Python; experience with Node.js or Go is a plus.
Solid computer science fundamentals, including algorithms, system design, database design, API architecture, distributed systems, caching, and performance optimization.
Production-level LLM application experience, not limited to demos or prototypes. Experience should include prompt engineering at scale, model selection, inference pipeline design, or RAG architecture.
Hands-on experience with RAG and vector databases such as Pinecone, Weaviate, Chroma, or similar tools.
Experience owning full engineering delivery, including backend services, basic frontend integration, API deployment, monitoring, and troubleshooting.
Heavy user of AI coding tools such as Cursor, Claude Code, GitHub Copilot, or similar tools.
Mandarin fluency is required; English working proficiency is required.
Able to work independently under ambiguous instructions and make sound technical decisions without waiting for detailed specifications.
Company
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