Applied Scientist

Our Client is a research-driven deep-tech company developing next-generation computing technology. The organisation brings together researchers, scientists, and engineers working on complex technical challenges.

The company is looking for a Lead Applied AI Engineer to identify opportunities where modern AI can meaningfully accelerate research and engineering workflows, and turn those opportunities into reliable, production-ready tools.

This is a hands-on, high-impact role for someone who can move beyond proof-of-concepts and demonstrations to build AI-powered solutions that are adopted in day-to-day work.

You will work closely with research, experimentation, design, and software engineering teams to understand their challenges, build practical solutions using modern language models and coding agents, validate results against real data, and establish reusable AI-enabled development practices.

We welcome candidates with strong applied AI engineering experience who are comfortable working across scientific and engineering disciplines.

Primary Responsibilities:

• Identify high-impact AI opportunities: Work directly with research and engineering teams to understand existing workflows, identify bottlenecks, and prioritize opportunities where AI can improve speed, quality, and repeatability.

• Build AI-powered solutions: Design, prototype, and implement applications using large language models (LLMs), AI agents, coding agents, and other appropriate technologies.

• Develop production-ready LLM applications: Implement agent workflows, tool and function calling, retrieval-augmented generation (RAG), and evaluation mechanisms.

• Validate solutions using real-world data: Collaborate with domain experts to test outputs against technical requirements, measurable quality criteria, and practical use cases.

• Productize successful prototypes: Develop reliable, maintainable software supported by appropriate testing, documentation, and deployment practices.

• Drive adoption across teams: Integrate AI-enabled tools into daily workflows and ensure colleagues can use, maintain, and improve them independently.

• Build reusable AI capabilities: Establish shared components, implementation playbooks, and engineering standards that can be adopted across multiple teams.

• Balance speed with reliability: Make sound technical decisions that enable rapid experimentation without compromising quality, usability, or long-term maintainability.

Required Qualifications:

• Proven experience working closely with non-software disciplines, such as scientific research, experimentation, product design, or engineering.
• Demonstrated ability to take a problem from discovery and requirements definition through implementation, deployment, and adoption.
• Experience building and operating LLM-powered applications that colleagues use regularly in their day-to-day work. Proof-of-concepts or demonstrations alone are not sufficient.
• Hands-on expertise in LLM application development, including prompt engineering, agent design, tool/function calling, RAG, and quantitative evaluation of output quality.
• Strong production-level Python development skills, including testing, code review, CI/CD, and technical documentation.
• Practical experience using AI coding agents as a core part of the development workflow, such as Claude Code, Codex, or comparable tools.
• The ability to rapidly prototype solutions while maintaining the reliability, usability, and maintainability required for production adoption.
• Strong communication and collaboration skills, with the ability to translate complex technical requirements into practical software solutions.

Preferred Qualifications:

• Professional working proficiency in both Japanese and English.
• Experience collaborating with international engineering teams, including technical design reviews and specification alignment.
• Experience delivering collaborative projects with research institutions, including proof-of-concept planning and requirements definition.
• An academic or professional background in physics, scientific/advanced computing or another deep-technology field.

What Success Looks Like:

In this role, success means creating measurable improvements in how research and engineering teams operate.

• Researchers and engineers spend less time on repetitive, fragmented, or manual tasks and more time on high-value technical decisions.
• AI-enabled tools deliver measurable improvements validated against real development data.
• Solutions move beyond experimentation into reliable, documented, maintainable daily workflows.
• Teams can operate and improve their AI tools independently, without ongoing reliance on a single engineer.
• Reusable components and best practices create a scalable internal AI engineering capability across the organisation.

If you have built AI-powered applications that solve real engineering problems, enjoy working directly with technical teams, and want to turn emerging AI capabilities into practical, production-ready solutions, please contact our recruitment partner for a confidential discussion.