AI-Native Full-Stack Engineer for Life Sciences

PatSnap; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

PatSnap in Singapore seeks a Full Stack AI Engineer (Life Sciences) to build AI-native products for pharma, biotech and R&D. You will create user experiences, backend services and intelligent workflows powered by LLMs, turning ideas from experiments into deployable features.

The role blends software fundamentals with experimentation across frontend, backend, data, and AI tooling to deliver reliable, scalable solutions for life sciences challenges.

Qualifications

• Bachelor's degree or above in Computer Science, AI, Bioinformatics or related field.
• Strong foundation in Python and production-grade coding skills.
• Proficient in React or Vue for frontend development.
• Ability to build full-stack solutions connecting UI, backend, data and LLMs.
• Experience integrating mainstream LLM APIs and tooling.
• Understanding of prompt engineering to improve model outputs.
• AI-native mindset with curiosity and rapid experimentation.

Responsibilities

• Build AI-native products end to end across frontend, backend and LLM-powered features.
• Prototype rapidly with frontier models, APIs, and open-source tools before production.
• Design and optimize intelligent workflows combining LLMs, data, and business logic.
• Tackle accuracy challenges to improve reliability of outputs.
• Work with life sciences use cases and translate problems into technical solutions.
• Create intuitive user experiences to enable scientists to use AI capabilities.
• Continuously experiment with models, prompts, and architectures.

Skills

Python

React/Vue

Full-stack

LLM APIs

Prompt engineering

AI-native mindset

Data-driven

Education

Bachelor's degree or above in CS/AI/Bioinformatics

Tools

LLM APIs

Job description

PatSnap in Singapore seeks a Full Stack AI Engineer (Life Sciences) to build AI-native products for pharma, biotech and R&D. You will create user experiences, backend services and intelligent workflows powered by LLMs, turning ideas from experiments into deployable features.

The role blends software fundamentals with experimentation across frontend, backend, data, and AI tooling to deliver reliable, scalable solutions for life sciences challenges.