Hi — I’m Kotaro Muramoto from JOBs Japan. Here’s a recent success story that shows what top technical skills + the right positioning can do for your career in Japan. 

Candidate snapshot

• Origin: Ethiopia.

• Strength: Hands-on AI / machine-learning development experience at global companies, plus full-stack engineering.

• Core tech: Node.js (backend), Vue.js (frontend), plus Python, Django, cloud platforms (AWS/Azure), LLM work and DevOps practices.
  This candidate could quickly prototype end-to-end features and had prior experience in architecture/tech-lead tasks.

The role & team

The hiring team builds AI products that analyze and optimize startup ecosystems worldwide using LLMs and other AI tech. They needed someone who can rapidly prototype and own both frontend and backend pieces (tech-lead level experience preferred). 

How the hire happened (what helped them get hired quickly)

• We ran a targeted search across Eastern Europe and Africa emphasizing strong skills and reasonable salary expectations for Japan’s market.

• The client interviewed 8 candidates over Zoom within two weeks; structured feedback via shared docs kept the process fast and aligned. The Ethiopian candidate was selected two weeks after the request. 

Why this candidate stood out (what you can copy)

1. End-to-end impact: Able to prototype UI + backend quickly — this reduces ramp-up time and makes you more valuable from day one. 

2. AI / LLM experience: Practical experience with LLMs and ML projects is rare and highly prized — especially for companies building AI products. 

3. Breadth + leadership: Experience in architecture and tech-lead work signals you can own a project, not just implement tasks. 

4. Cloud + DevOps competence: Familiarity with AWS/Azure and DevOps practices showed the company they wouldn’t face surprises in production. 

Result

• Offer & start: The candidate was offered the role and is expected to contribute to strengthening the team’s technical foundation and AI product roadmap. Annualized compensation in the case posted: ¥5,700,000. 

Quick advice if you want a similar outcome

• Highlight one or two full projects (what problem, your design/architecture, tech choices, impact). Concrete stories beat vague skill lists.

• If you have LLM or ML experience, spell out the dataset, evaluation approach, and production details (latency, throughput, infra). Recruiters love specifics.

• Show you can move fast: include examples of prototypes, MVPs, or PoCs you shipped.

• Be ready to speak to cloud and deployment practices — companies hiring AI need ops-aware engineers.

• Use collaboration tools (shared docs, GitHub) to make the interview process frictionless — it helps speed up hiring.