AI Development Experience in Japan

Executive Summary

Senior Software Engineer with hands-on experience building production AI systems, LLM workflows, and AI-augmented engineering solutions across automotive and enterprise domains.

Focus Areas

  • Production AI Systems
  • LLM Integration
  • AI Workflow Engineering
  • Real-Time AI Applications
  • AI-Augmented Software Engineering

1. Toyota AI Voice Sales Assistant

Period: 2022.11 - 2023.01

Designed a real-time AI assistant for sales conversations. The system analyzed customer speech during the call and surfaced key signals such as:

  • budget
  • family situation
  • target vehicle
  • financing intent

System Architecture

  • Frontend: PowerApps / Flutter
  • API gateway: WebSocket
  • Backend: Lambda (Python)
  • Speech processing: Azure Speech SDK
  • Morphological analysis: MeCab
  • Text analysis: Azure Text Analytics
  • Storage: RDS / S3
  • Output: live indicators on the frontend

Key Technologies

  • AWS Lambda
  • Azure Speech SDK
  • Azure Text Analytics
  • Python
  • WebSocket
  • MeCab
  • RDS

Engineering Challenges

  • Real-time speech streaming
  • Speaker separation
  • Timestamp alignment across services
  • Lambda lifecycle recovery
  • Multi-service orchestration

Technical Highlights

  • Combined speech recognition and language analysis in a streaming workflow
  • Used a hybrid cloud setup with Azure Speech for transcription and AWS Transcribe for speaker diarization
  • Aligned results from multiple speech services using timestamp-based fusion
  • Normalized timestamp differences between 100ns ticks and second-based outputs
  • Distinguished customer speech from salesperson speech
  • Designed checkpoint-based recovery for unstable Lambda lifecycles
  • Decoupled high-frequency audio streaming from lower-frequency business logic
  • Reassembled WebM audio chunks inside Lambda and converted them into continuous streams for recognition
  • Defined a message protocol for multi-Lambda coordination
  • Stored aggregated results in RDS

Business Result

The sales team could see live indicators on the tablet while speaking with the customer. This made the assistant useful as a real-time sales copilot.

2. AI Workflow Refactoring for n8n + LLM

Period: 2025 Q3

I refactored a prediction workflow for better maintainability, observability, and cost control.

Technical Work

  • Split large workflows into smaller stages
  • Reduced workflow execution time from hours to minutes
  • Lowered token consumption and operating cost
  • Improved observability and maintainability
  • Moved complex business logic into backend services for better scalability
  • Added automation around repeatable steps

Architecture Notes

  • Replaced serial, one-record-at-a-time processing with staged batch execution
  • Moved payload thinning and validation into the backend layer before model calls
  • Used workflow nodes for orchestration, not for heavy business logic

Technical Outcome

The implementation reduced end-to-end execution time from roughly 3 hours to about 8 minutes and lowered operating cost to around $0.13 per run. The workflow also became easier to operate, debug, and extend.

3. LLM-Driven Engineering for Large Systems

Period: 2025.08 - 2026.01

I used LLM tools to accelerate understanding and delivery across backend, web, network, and cloud systems.

Main Activities

  • Accelerated understanding of unfamiliar codebases
  • Decomposed large tasks into executable engineering steps
  • Troubleshot across backend, frontend, and infrastructure boundaries
  • Used LLM-based tools to speed up code comprehension and implementation
  • Recovered system context faster during multi-project delivery
  • Kept the AI role as an engineering multiplier, not a replacement for design judgment

Tooling

  • Claude Code
  • OpenAI models
  • Kilo Code
  • GLM

Practical Positioning

I worked as a senior backend engineer and used AI tools to accelerate implementation, explanation, and learning. In practice, this meant I could handle backend work and the necessary frontend or integration work when needed.

Summary

My AI experience focuses on production AI systems and AI-enabled software engineering.

The common pattern across these projects is:

  • combining AI services, cloud platforms, and real-time systems
  • integrating speech, text, workflow, and backend components
  • solving reliability, latency, and maintainability problems
  • delivering production-grade systems
  • using AI as a force multiplier for software engineering