Engineering Team Lead – AI Applications (Life Sciences)

Role Overview

We are looking for a hands-on engineering leader who combines AI application architecture expertise with strong software engineering capabilities. You will work closely with the team on architecture reviews, code reviews, and prompt optimization, while leading the development of AI-native applications that transform digital workflows in the life sciences industry. 

Technical Architecture: Own the overall technical architecture of AI applications for the life sciences domain. Lead the design and implementation of complex agentic workflows, multi-agent collaboration mechanisms, and solutions involving LLM fine-tuning or private deployment.

Technical Problem-Solving: Lead the team in addressing critical challenges such as LLM hallucinations, complex biomedical reasoning, and high-precision information extraction. Develop agent planning, tool-use, and reflection mechanisms to improve the accuracy, reliability, and domain relevance of AI-generated outputs.

Engineering Leadership: Lead, mentor, and develop an AI full-stack engineering team. Establish modern development workflows powered by AI-assisted coding tools and continuously improve engineering quality and delivery efficiency.

Cross-functional Collaboration: Work closely with life sciences experts and product managers to translate complex medical and pharmaceutical requirements into practical AI solutions and agent-based applications.

Hands-on Contribution: Remain actively involved in technical design, architecture and code reviews, prompt optimization, and the resolution of complex engineering challenges.

At least 5 years of experience in software or full-stack development, including 2 or more years of hands-on experience delivering AI or LLM-powered applications.

Proven experience leading an agile software engineering team.

Strong understanding of the capabilities, limitations, and underlying principles of large language models.

Hands-on experience with agent frameworks such as LangChain, AutoGen, or CrewAI, as well as common agent orchestration patterns.

Proven experience developing, deploying, and optimizing AI-native applications for complex business scenarios.

Strong commitment to code quality, software architecture, and production-grade engineering practices.

Proficiency in Python and at least one statically typed programming language.

Strong knowledge of modern frontend and backend architectures, as well as cloud-native deployment.

An AI-native mindset and the ability to redesign complex workflows using LLMs and agents.

Professional working proficiency in English, with strong written, verbal, and cross-cultural communication skills.