Lead and contribute to end-to-end solutions powered by Large Language Models (LLMs)
Drive innovation and practical applications of LLMs to solve complex business problems
Collaborate across teams to deliver scalable, value-driven LLM systems
Responsibilities
LLM-Centric Solution Development:
Design, fine-tune, and implement cutting-edge LLMs for business use cases such as content generation, intelligent assistants, semantic search, or knowledge retrieval
Evaluate and select appropriate foundation models (e.g., GPT, LLaMA, Mistral) and tailor them to organization-specific domains.
Build modular, scalable pipelines for LLM applications, including prompt engineering, inference optimization, and feedback loops
Collaborative Project Contribution:
Work closely with Machine Learning Engineer, Data Engineer, DevOps Engineer, Developers and stakeholders to define project goals and align model capabilities with business outcomes
Translate high-level requirements into actionable LLM development plans with measurable KPIs
Support the lifecycle of LLM initiatives from experimentation to deployment
Technical Excellence:
Stay current with the rapidly evolving LLM landscape, including open-source models, toolkits (e.g., Hugging Face, LangChain), and retrieval-augmented generation (RAG) frameworks
Optimize cost, performance, and latency for LLM inference at scale
Ensure reproducibility, quality, and efficiency across LLM workflows
Effective Communication & Collaboration:
Present technical findings and solution designs clearly to both technical and non-technical audiences
Share knowledge and contribute to internal LLM best practices and education
Offer technical support and informal mentoring on LLM topics across teams
Requirements
Bachelor's or Master's in Computer Science, Artificial Intelligence, Data Science, or a related field
2+ years of industry experience in machine learning or data science, including at least 1 year focused on LLM development or deployment
Proficient in Python and frameworks relevant to LLM development (e.g., PyTorch, Hugging Face Transformers, LangChain, OpenAI API)
Practical experience in fine-tuning, evaluating, and integrating LLMs into applications at scale
Familiarity with prompt engineering, vector databases (e.g., FAISS), and RAG or LLMOps pipelines
Strong analytical and problem-solving skills with the ability to work independently or in cross-functional teams
Proficient in English (spoken and written); Japanese skills are a plus but not required
