<Expectation for the role>

• Near Real-Time Conversational Agents – design and deploy multi-modal, tool-using agents that classify inquiries, ask clarifying questions, and draft estimates (RAG pipelines, function-calling, etc.).
• Vector search & knowledge graphs – build and tune semantic search over Firestore +
• Weaviate, exploring graph-based representations where useful.
• Model evaluation – establish repeatable benchmarks, offline/online metrics, and automated regressions so we know when a new prompt or fine-tune is truly better.
• Prototyping → Production – craft PoCs in notebooks, then convert the winners to clean, tested services running on Cloud Run (Python FastAPI, occasional Go/Rust helpers).
• Collaboration – pair closely with product, and design to ship features end-to-end.

<Must Have Requirements>

• Preferably graduated from a top Vietnamese university (HCMUT, HCMUS, HUST, UIT, etc.) and/ or top university around the world.
• Have 4–5 yrs building ML or data-intensive systems (industry, or advanced grad work).
• Write clean Python and are fluent in at least one deep-learning framework (PyTorch
• preferred).
• Understand the maths enough to debug when a model or retrieval step misbehaves.
• Deeply understand the mechanics of RAG, vector databases, and prompt engineering, and can debug when a retrieval step or LLM response is suboptimal.
• Communicate clearly in English (Japanese a plus) and enjoy explaining trade-offs to non-ML teammates.
• Like the idea of being the first dedicated ML hire and setting best practices from scratch.
• Language: Working-level proficiency in English.

<Nice to Have>

• Hands-on with Google's AI stack (Vertex AI, Gemini Models, Agent Builder, BigQuery).
• Experience developing AI applications using AWS Bedrock
• Experience developing AI applications using Google Cloud AI
• Experience developing with AI-assisted editors or agents such as Claude Code, Cursor, or Windsurf
• Experience building applications that utilize Generative AI or Large Language Models (LLMs)
• Experience in fine-tuning or distillation of LLMs for multilingual tasks (e.g., Japanese
• and English)
• Experience operating vector databases and evaluation tools such as Weaviate
• Proven record of public technical contributions, such as open-source projects or technical blog publications

• LLM / Agents: OpenAI, Ollama (local LLMs), LangChain 
• Retrieval: Firestore + vector DB, Cloud Storage 
• Serving: Python FastAPI, Cloud Run, Docker/Podman 
• CI/CD: GitHub Actions, Terraform (coming) 
• Monitoring: Cloud Logging, Prometheus, OpenTelemetry

<Benefits>

• Paid Vacations
• Annual Bonus: 1-month salary

<Note> 

• This is a full-time position requiring 40 hours per week, but it will be structured as contractor work.
• Devices: You will be expected to use your own computer to perform the work.
• Sole Employment: No second job is permitted.

_______________________________

If you are interested in this position, please:

• Email [email protected] if you have any questions
• Send your CV through application form:
• https://sheets.qlay.ai/dashboard/#/nc/form/5658d2ad-01db-4fe5-bce2-0833cf3de095