Applications: candidates must already be in Japan. Visa sponsorship: not available. Japanese: Business level. English: Business level. Japan Dev tags: Engineering, ML, Python, Kubernetes, Docker, PostgreSQL, SQL.
Recruitment Background
VISASQ operates a knowledge platform dedicated to its mission: "We make insightful connections possible." By leveraging an extensive global network of over 800,000 experts, we connect businesses challenges, from new business development and HR strategies to global expansion, with individuals possessing precisely the right knowledge. Our tailored solutions, including one-hour interviews, online surveys, and dedicated hands-on support, empower companies to overcome obstacles and achieve their most ambitious goals.
As a pioneer in Japan's Expert Network Service (ENS) industry, having acquired Coleman with the aim of becoming a Global Knowledge platform,we have just entered an ambitious new AI chapter: automating the diffusion of unique human insights that major LLMs haven't captured yet.
To achieve this, we are seeking Software Engineers, ML Engineers, and Data Scientists who can contribute to both engineering and architecture design by taking charge of designing and implementing autonomous AI agents leveraging predictive and generative models.
We are looking for Mid-level and Senior Talent. VisasQ is a publicly listed company (founded in 2012 and still led by our founder/CEO) that operates with a fast-paced startup/scale-up mindset. We are actively strengthening our core team in Japan while collaborating with our engineering presence across the US and Europe.
Responsibilities
- Collaborating with global English-speaking engineering team in Japan, US and Europe
- Designing, implementing, and validating machine learning models and AI agents across multiple providers (OpenAI, Anthropic, Google) to ensure cost-optimization and system resilience
- Promoting CI/CD pipelines, training, model monitoring, and version control based on MLOps best practices
- Optimizing machine learning model performance and scalability
- Fine-tuning large language models (LLMs) and designing/implementing training
- Stay current with the latest trends in machine learning, MLOps, and LLMs; researching and proposing new technologies and methodologies
Highlights of the Role
- End-to-End Ownership: Work closely with the business side throughout the entire lifecycle, from initial problem-solving and specification design to release and continuous iteration.
- Proactive Innovation: We don't just hand you tickets; engineers are expected and encouraged to propose their own ideas and shape the product roadmap from the planning stage.
- Global Mobility: Depending on your performance and preferences, there are opportunities to work at our New York development hub to accelerate your global career.
Technical Environment
- Languages: Python, SQL
- AI/ML & Orchestration: Azure OpenAI, Gemini, Anthropic, LangChain, LangGraph, Scikit-learn, Transformers
- Backend & Web: FastAPI, Streamlit, SQLAlchemy
- Data & Search: Azure Cognitive Search, Elasticsearch, Redis, CosmosDB, PostgreSQL
- Infrastructure & DevOps: Docker, Kubernetes, Azure Functions, Azure DevOps
Required Qualifications
- Business-level English proficiency for cross-border collaboration with US and European teams.
- Production ML & Backend Experience: Deep hands-on experience building production-grade backend services and ML pipelines.
- Mid-level: 3+ years of experience; comfortable navigating large, multi-module Python codebases independently.
- Senior-level: 5+ years of experience; able to architect event-driven workflows, optimize system latency, and design hybrid search retrieval.
- Core Technical Competency:
- Experience with LLM orchestration (structured outputs, tool use, multi-step agent workflows).
- Practical experience with tabular ML pipelines (feature engineering, ranking models/LTR, model validation).
- Familiarity with modern DevOps/MLOps (Docker, pytest/mocking, CI/CD pipelines).
Preferred Qualifications
- Cloud Infrastructure: Experience deploying and scaling services in public cloud environments
- Product Growth: Track record of iteratively scaling products in a fast-paced, cross-functional startup or scale-up environment.