Data Scientist (Machine Learning /GenAI)

Requirements.Key ResponsibilitiesWork closely as a project team as the main contributor in the entire lifecycle of data science projects, from gathering business requirements to solution delivery, and deliver project outcomes to internal stakeholders or external customers.Engages with clients to understand business needs, present findings, and align technical work with stakeholder goals.Provides structured feedback to junior members, supports onboarding, and contributes to skill-building within the project team.Developing scalable AI systems and designing/building algorithms that automate predictive data models.Creating and managing the AI development and production infrastructure, including automating AI infrastructures for data science teams.Participating in all phases of AI solutions development, from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring.Collaborating extensively with cross-functional teams, including data scientists, software engineers, and product managers, to define project requirements, ensure seamless progress, and facilitate AI adoption and best practices.Driving scalability improvements and introducing new capabilities in machine learning platforms, often working across the full stack on tools and infrastructure that empower machine learning teams.Required Skills & QualificationsA Bachelor's degree in a technical field such as CS, AI, or Data Science, or equivalent practical experience. A Master’s degree is a plus.Junior-Middle: 2-3 years, Senior: 4-5 years of professional experience building and shipping software, with a focus on AI/ML systems.Familiarity with cloud platforms (AWS, Azure, GCP).Proficiency with containerization (Docker, Kubernetes) and modern CI/CD practices for production environments.Experience in building or prototyping features with GenAI capabilities (LLMs, Embeddings, Fine-Tuning) and core NLP concepts.Experience in agentic frameworks (eg. LangChain, MCP, AutoGen) for building LLM-powered applications.Experience with reinforcement learning, especially RLHF or applying RL to agent-based systems.Able to communicate effectively within the team members.The ability to work independently, troubleshoot complex issues, and mentor more junior team members is also expected.Strong problem-solving skills and a data-driven mindset are fundamental.Develop and Implement Evaluation Frameworks: The engineer will be responsible for creating and deploying frameworks to test and benchmark generative AI solutions for both quantitative and qualitative metricsExperience with high-performance computing, GPU optimization, and building for low-latency environments is a strong plus.Fluent in English with strong communication skills, especially when engaging with non-technical business stakeholders.BenefitWhat We Offer 100% salary during probation05 working days/week (From Monday to Friday), applying flexible working hours2 days of remotely WFH per week (based on team's decision)Lunch + Gasoline + Coffee AllowanceHealth, Social and Unemployment Insurance (based on gross-based salary, according to Labor Code) and PVI Health Insurance13th-month salary and Performance bonusAnnual salary review12 days annual leave plus extra 02 days company leaveCompany trips, sponsored team building, monthly Happy Hour, Sport Clubs (Soccer, Badminton, Pingpong, Yoga) and other joyful events;A culture of relentless learning with free courses in specialized skills, soft skills, and English;Yearly health-checkup;Seniority benefits: allowance & PVI Health Insurances for family membersTechnical-certificate bonusJapanese-certificate bonusEmployee Referral Incentive