No Japanese Required AI Engineer (RAG Specialist)

Key Responsibilities

RAG System Design & Operation

• Architect and implement RAG-based systems
• Build and optimize vector databases (e.g., FAISS, Elasticsearch, Pinecone)
• Develop document preprocessing and chunking strategies

Maintenance & Monitoring

• Operate and monitor RAG systems in production
• Analyze performance, identify bottlenecks, ensure stability

Accuracy Improvement

• Evaluate and improve retrieval/response quality
• Apply prompt engineering and model fine-tuning
• Define and implement evaluation metrics

R&D and Technical Validation

• Explore and validate cutting-edge RAG methods
• Build POCs (Proof of Concept)
• Contribute to architecture and tool decisions

Development

• Build and extend RAG-related features
• Design and implement APIs
• Contribute to both frontend/backend development when needed

Team Structure

Our development team consists of approximately 65 members, structured into the following groups:

• Client-Facing Solution Development
• In-house AI SaaS Development
• Common Platform Development (Infra / Data / AI R&D)

Minimum Qualifications

• Bachelor’s degree or equivalent experience in CS, AI, ML, Mathematics, or related fields
• Hands-on experience designing, building, and operating RAG systems
• Experience with vector DBs (e.g., FAISS, Elasticsearch, Pinecone)
• Document preprocessing and chunking strategies
• Production monitoring and incident response
• System optimization and performance tuning
• Evaluation and improvement of search/generation quality
• Prompt engineering & fine-tuning
• Metric design and implementation
• API development (e.g., RESTful APIs)
• Python development (including ML/NLP libraries)
• Experience with cloud (AWS, GCP, etc.)
• Team collaboration and communication skills
• English: Business level or above

Preferred Qualifications

• Frontend/backend experience (React, Vue.js, Flask, FastAPI, etc.)
• LLM tuning and evaluation experience
• CI/CD experience (GitHub Actions, Jenkins)
• Containerization (Docker, Kubernetes)
• Familiarity with RAG-related academic papers and trends
• POC development experience
• Data engineering knowledge (ETL, pipelines)
• Strong reading skills of English technical documents

Tech Stack / Tools

• Languages: Python (Backend), TypeScript / React / Next.js (Frontend) / NX
• Infrastructure: GCP (Kubernetes), Docker
• Tools: Slack, Confluence, Linear, Google Workspace, GitHub, Notion
• Hardware: Mac (Apple Silicon), dual monitors