Business Unit:

• Corporate Risk Office

Responsibilities and selected work:

• Architect and operationalize enterprise AI/ML solutions, including autonomous AI Agents and MCP-based LLM integrations, with data governance, security, and responsible AI practices at the core.
• Built an AI-assisted corporate risk intelligence pipeline that extracts and normalizes risk disclosures from 56-1/10-K filings, maps risk drivers and risk items, clusters semantically related risks, compares sector and industry patterns, and supports peer-based identification of potential risk blind spots with source traceability.
• Collaborate with data and software engineers to deploy AI/ML solutions across CPG subsidiaries, including CPALL, CPF, CPAXT, and True Corporation; designed internal LLM knowledge systems using fine-tuned LLaMA models and the ChatGPT API.
• Built location-intelligence and demand-forecasting solutions using geospatial data, POI, store profiles, Transformers, SARIMAX, Huff's gravity model, and SHAP to support branch-expansion and retail decision-making.
• Lead ML model lifecycle management with MLflow and Weights & Biases, advise cross-functional teams on AI best practices and model governance, and evaluate emerging AI/ML techniques for business use cases.

Department:

• ARV Core

Responsibilities include:

• Develop in-house machine-learning technology in the field of computer vision.
• Build object-detection models to identify crack defects on flare stacks at offshore oil rigs.
• Deploy and maintain machine-learning API services and experiment with MLOps technologies on existing services.
• Support ARV's subsidiary companies with AI and machine-learning solutions.
• Contribute to the company's AI Studio platform during its initiation stage.

ML models and application tool stack:

• ML frameworks: PyTorch, YOLOv5.
• Application frameworks: FastAPI/Django, REST API, Docker, Celery with RabbitMQ, SQLAlchemy, PostgreSQL.
• Deployment: GitLab Runner; AWS API Gateway, Lambda, Application Load Balancer, ECS Fargate, ECR; etc.

Site location:

• UACJ R&D Center, Nagoya, Aichi, Japan

Business Unit:

• 先端生産技術研究室 (Advanced Production Technology Research Section)

Responsibilities include:

• Develop machine-learning models to improve materials-manufacturing processes.
• Develop a web-application platform to deploy ML models using Django, REST API, and jQuery.
• Optimize manufacturing processes with machine-learning techniques to improve efficiency and KPIs, reduce costs, and prevent failures.

ML methodologies and frameworks:

• Logistic and multiple regression, multilayer perceptrons, CNNs, Grad-CAM, RNN/LSTM, autoencoders, k-means clustering, Bayesian optimization, Gaussian processes, and MCMC.
• Keras–TensorFlow, Chainer, SciPy, NumPy, pandas, scikit-learn, scikit-optimize, Hyperopt.
• Django, REST API, vanilla JavaScript, jQuery, Chart.js, Apache Web Server, and private internal Windows Server.

Selected achievement:

• Developed a Python-based data-analytics web application for production engineers with ML-based prediction, visualization, and optimization, enabling interactive optimization of manufacturing parameters such as alloy composition, cold-rolling reduction rate, and heat-treatment time.

Research sessions:

• Supramolecular Science Laboratory, Division of Materials Science — under Prof. Shun Hirota. Research theme: polymerization of cytochrome c heme protein. Techniques included biosynthesis, ion-exchange chromatography, and size-exclusion chromatography.
• Advanced Polymer Science Laboratory, Division of Materials Science — under Prof. Michiya Fujiki. Research theme: computer simulation and synthesis of π-conjugated polymers. Techniques included density-functional theory, polymerization, and nuclear-magnetic-resonance spectroscopy.