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I'm Yosuke Kuroki, an AI/Machine Learning Engineer focused on building scalable, production-grade AI systems. I thrive when turning ideas into fast-moving products—ranging from zero-to-one prototypes to large-scale deployments—using NLP, computer vision, OCR, and MLOps.\n\nI’ve led projects across SaaS, enterprise, and fintech domains, including developing LLM-powered tools, vector search architectures, and efficient OCR pipelines. I’m passionate about delivering impactful AI at speed and mentoring teams to raise their technical bar.…I'm Yosuke Kuroki, an AI/Machine Learning Engineer focused on building scalable, production-grade AI systems. I thrive when turning ideas into fast-moving products—ranging from zero-to-one prototypes to large-scale deployments—using NLP, computer vision, OCR, and MLOps.\n\nI’ve led projects across SaaS, enterprise, and fintech domains, including developing LLM-powered tools, vector search architectures, and efficient OCR pipelines. I’m passionate about delivering impactful AI at speed and mentoring teams to raise their technical bar.

Yosuke Kuroki

Full Stack Developer, AI Developer, Data Scientist, +2

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I'm Yosuke Kuroki, an AI/Machine Learning Engineer focused on building scalable, production-grade AI systems. I thrive when turning ideas into fast-moving products—ranging from zero-to-one prototypes to large-scale deployments—using NLP, computer vision, OCR, and MLOps.\n\nI’ve led projects across SaaS, enterprise, and fintech domains, including developing LLM-powered tools, vector search architectures, and efficient OCR pipelines. I’m passionate about delivering impactful AI at speed and mentoring teams to raise their technical bar.…I'm Yosuke Kuroki, an AI/Machine Learning Engineer focused on building scalable, production-grade AI systems. I thrive when turning ideas into fast-moving products—ranging from zero-to-one prototypes to large-scale deployments—using NLP, computer vision, OCR, and MLOps.\n\nI’ve led projects across SaaS, enterprise, and fintech domains, including developing LLM-powered tools, vector search architectures, and efficient OCR pipelines. I’m passionate about delivering impactful AI at speed and mentoring teams to raise their technical bar.

Available to hire

I’m Yosuke Kuroki, an AI/Machine Learning Engineer focused on building scalable, production-grade AI systems. I thrive when turning ideas into fast-moving products—ranging from zero-to-one prototypes to large-scale deployments—using NLP, computer vision, OCR, and MLOps.\n\nI’ve led projects across SaaS, enterprise, and fintech domains, including developing LLM-powered tools, vector search architectures, and efficient OCR pipelines. I’m passionate about delivering impactful AI at speed and mentoring teams to raise their technical bar.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

SR ML/LLM, Generative AI engineer at Ultim Group

January 1, 2025 - Present

Designed a novel GPT-4o-based evaluation framework for long-text generation and summarization, reducing feedback cycles from days to minutes and enabling rapid iteration. Spearheaded R&D for Client Intelligence, leveraging LLMs to extract structured insights from historical broker chatrooms and developing a text-to-SQL chatbot for natural language retrieval. Built automated ingestion and preprocessing pipelines using Azure AI and cloud functions, and embedded unstructured documents into a vector database for downstream search. Scaled an AI document processing platform by 100x (17k+ tenants/year), optimizing UI/UX and prompt engineering, and doubling analyst efficiency, saving 8.5k+ hours annually. Led a major system refactor (10k+ LOC), slashing per-tenant costs by 80% and reducing latency from 45 minutes to 5. Led the development of a CNN-based OCR model optimized for GPU inference, reducing latency by 41% with TensorRT and Docker.

.NET, Full Stack Developer(Contract Position) at ZOJAX GROUP

November 1, 2024 - Present

Full-stack web development using .NET, JavaScript, and modern frameworks to build scalable, high-performance applications for enterprise clients. Collaborated with cross-functional teams (product, UX, QA) to design, develop, and deploy end-to-end solutions that enhance user experience and business efficiency. Optimized application performance through code refactoring, database tuning, and cloud integration (Azure/AWS). Ensured maintainability by implementing clean architecture, RESTful APIs, and CI/CD pipelines.

Chief Technology Officer at SPOTCUBE.INC

December 1, 2024 - September 10, 2025

Spearheaded the creation of a hyperspectral tree species classifier, achieving a 30% increase in accuracy over traditional RGB methods, enhancing the company's technological edge in environmental analytics. Implemented a LiDAR-RGB fusion pipeline that significantly reduced false negatives in object detection by 30%. Redesigned the training infrastructure, reducing model development cycles from 7 days to 20 hours while cutting AWS costs by 80%, thereby maximizing resource efficiency. Co-developed a graph neural network architecture that halved the processing time for 3D point clouds. Streamlined annotation workflows, resulting in a 50-70% increase in labeling team efficiency, and mentored mid-level developers.

Machine Learning Engineer at CTI-Construction Testing & Inspection, Inc.

February 1, 2022 - September 10, 2025

Developed a medicine ranking system leveraging Python, Databricks, and SparkXGBRanker, implementing EDA, feature engineering, and hyperparameter tuning to enhance model accuracy. Managed cloud infrastructure on AWS EKS and Azure Kubernetes Service, provisioning resources via Terraform and Helm. Designed and deployed scalable data pipelines on AWS S3/EC2 & Google Cloud Storage, optimizing preprocessing and model training for medical imaging datasets. Enhanced model performance using data augmentation, transfer learning, and cross-validation techniques, improving robustness for real-world clinical applications. Containerized ML applications using Docker, orchestrating secure, HIPAA-compliant deployments with Kubernetes to ensure real-time inference. Deployed ranking models as UISE JVM Chassis applications, implementing real-time tracking, alerting, and periodic retraining. Refactored and migrated legacy ML pipelines to Java & gRPC/OIPx protocol, modernizing a low-latency orchestrator for

Machine Learning developer at Google

February 1, 2020 - September 10, 2025

Developed a CNN-based OCR Text Detection Model, reducing inference time for scanned documents and card images, optimizing processing speed and accuracy. Designed and fine-tuned an OCR pipeline using Tesseract, digitizing and extracting textual metadata from archival labels, handwritten notes, and printed documentation. Customized OCR models to recognize specialized fonts and handwriting styles in historical records, enhancing digital metadata enrichment. Integrated denoising, text detection, and Tesseract OCR, leading to reductions in inference time for scanned certificate documents and PDFs. Developed computer vision-based techniques for character & table detection in document scene images, leveraging OpenCV to enhance document processing efficiency. Deployed distributed document processing pipelines on AWS (EC2, S3), ensuring scalability and secure high-volume storage of legal documents. Developed cloud-based RESTful APIs that seamlessly integrated ML models with legacy e-discovery s

Full Stack developer at Metadata

February 1, 2019 - September 10, 2025

Integrated real-time performance monitoring dashboards with Datadog, enhancing model observability and troubleshooting capabilities. Designed automated ETL & preprocessing pipelines for legal & archival datasets, ensuring efficient data ingestion & transformation on AWS Lambda & SageMaker. Conducted load testing with K6 & Locust, documenting findings & optimizing document processing pipelines for scalability & speed. Designed end-to-end NLP pipelines to automate the classification, review, and summarization of legal documents, reducing manual e-discovery time. Collaborated with legal teams & compliance specialists to validate model fairness & transparency, ensuring ethical AI deployment in litigation support systems.

SR ML/LLM, Generative AI engineer at Ultim Group

January 1, 2025 - Present

Designed a GPT-4o-based evaluation framework for long-text generation and summarization, reducing feedback cycles from days to minutes and enabling rapid iteration. Led R&D for Client Intelligence, leveraging LLMs to extract structured insights from broker chatrooms and developing a text-to-SQL chatbot for natural language retrieval. Built automated ingestion and preprocessing pipelines using Azure AI and cloud functions, and embedded unstructured documents into a vector database for downstream search. Scaled an AI document processing platform to 17k+ tenants/year, optimizing UI/UX, refining SME-driven prompt engineering, and doubling analyst efficiency—saving 8.5k+ hours annually. Led a major system refactor (10k+ LOC), reducing per-tenant costs by 80% and latency from 45 minutes to 5. Led the development of a CNN-based OCR model optimized for GPU inference, reducing latency by 41% with TensorRT and Docker.

.NET, Full Stack Developer (Contract Position) at ZOJAX GROUP

November 1, 2024 - Present

Full-stack web development using .NET, JavaScript, and modern frameworks to build scalable, high-performance applications for enterprise clients. Collaborate with cross-functional teams (product, UX, QA) to design, develop, and deploy end-to-end solutions that enhance user experience and business efficiency. Optimize application performance through code refactoring, database tuning, and cloud integration (Azure/AWS). Ensure maintainability and scalability by implementing clean architecture, RESTful APIs, and CI/CD pipelines.

Chief Technology Officer at SPOTCUBE.INC

December 1, 2024 - September 10, 2025

Spearheaded the creation of a hyperspectral tree species classifier, achieving a 30% increase in accuracy over traditional RGB methods, enhancing the company's technological edge in environmental analytics. Implemented a LiDAR-RGB fusion pipeline that significantly reduced false negatives in object detection by 30%. Redesigned the training infrastructure, reducing model development cycles from 7 days to 20 hours and cutting AWS costs by 80%. Co-developed a graph neural network architecture that halved the processing time for 3D point clouds, streamlining workflows and enhancing project turnaround times. Streamlined annotation workflows, resulting in a 50-70% increase in labeling team efficiency, and mentored mid-level developers to boost skills and productivity.

Machine Learning Engineer at CTI-Construction Testing & Inspection, Inc.

February 1, 2022 - September 10, 2025

Developed a medicine ranking system using Python, Databricks, and SparkXGBRanker, including EDA, feature engineering, and hyperparameter tuning. Managed cloud infrastructure on AWS EKS and Azure Kubernetes Service; designed and deployed scalable data pipelines on AWS S3/EC2 and Google Cloud Storage. Improved model robustness with data augmentation, transfer learning, and cross-validation. Containerized ML apps with Docker and deployed HIPAA-compliant real-time inference with Kubernetes. Deployed ranking models as UISE JVM Chassis applications with real-time tracking, alerting, and retraining. Refactored legacy ML pipelines to Java and gRPC/OIPx protocol, modernizing a low-latency orchestrator for high-frequency finance. Established CI/CD on AWS & GCP and designed ETL pipelines with Pandas, PySpark, Jenkins, Airflow, Databricks, and Querybook; automated ETL with AWS Lambda, EC2, and SageMaker; tuned Hadoop & Spark to optimize critical workloads.

Machine Learning developer at Google

February 1, 2020 - September 10, 2025

Developed a CNN-based OCR text detection model, reducing inference time for scanned documents and card images. Fine-tuned OCR pipelines with Tesseract to digitize metadata from archival labels, handwritten notes, and printed documents. Integrated denoising, text detection, and Tesseract OCR to accelerate processing of scanned certificates and PDFs. Built computer vision techniques for character and table detection in document images using OpenCV. Deployed distributed document processing pipelines on AWS (EC2, S3) and built cloud-based RESTful APIs to integrate models with legacy e-discovery systems. Created synthetic data for logo detection and deployed ranking models with Docker and Kubernetes as part of UISE JVM Chassis applications, enabling real-time monitoring and retraining.

Full Stack Developer at Metadata

February 1, 2019 - September 10, 2025

Integrated real-time performance dashboards with Datadog, designed automated ETL and preprocessing pipelines for legal and archival datasets on AWS Lambda & SageMaker. Conducted load testing with K6 & Locust, and optimized document processing workflows for scalability and speed. Built end-to-end NLP pipelines to automate classification, review, and summarization of legal documents; collaborated with legal teams to validate model fairness and transparency. Developed automated ETL pipelines on AWS Lambda, EC2, and SageMaker; deployed with RESTful APIs; supported migration of legacy pipelines to modern architectures.

SR ML/LLM, Generative AI Engineer at Ultim Group

January 1, 2025 - Present

Designed a GPT-4o-based evaluation framework for long-text generation and summarization; built automated ingestion pipelines using Azure AI and cloud functions; embedded unstructured documents into a vector database for downstream search; scaled an AI document processing platform to 17k+ tenants/year; led a major system refactor reducing per-tenant costs by 80% and latency from 45 minutes to 5; developed a CNN-based OCR model optimized for GPU inference.

Full Stack Developer (Contract Position) at ZOJAX GROUP

November 1, 2024 - Present

Full-stack web development using .NET and modern frameworks; collaborated across product, UX, and QA to design, develop and deploy end-to-end solutions; optimized performance via refactoring, database tuning, and cloud integration; implemented clean architecture, RESTful APIs, and CI/CD pipelines.

Chief Technology Officer at SPOTCUBE.INC

December 1, 2024 - September 10, 2025

Led hyperspectral tree species classifier achieving 30% accuracy improvement; implemented LiDAR-RGB fusion to reduce false negatives by 30%; redesigned training infrastructure cutting model development cycles from 7 days to 20 hours and reducing AWS costs by 80%; co-developed a graph neural network for faster 3D point cloud processing; streamlined annotation workflows increasing labeling efficiency by 50-70% and mentored mid-level engineers.

Machine Learning Engineer at CTI-Construction Testing & Inspection, Inc.

February 1, 2022 - September 10, 2025

Developed a medicine ranking system using Python, Databricks, and SparkXGBRanker; built scalable ETL pipelines on AWS S3/EC2 and Google Cloud Storage; enhanced model robustness with data augmentation, transfer learning, and cross-validation; containerized ML apps with Docker and deployed HIPAA-compliant real-time inference with Kubernetes; migrated legacy ML pipelines to Java and gRPC for low-latency analytics; established CI/CD across AWS and GCP.

Machine Learning Developer at Google

February 1, 2020 - September 10, 2025

Developed a CNN-based OCR text detection model; tuned OCR pipeline with Tesseract for digitizing archival labels and handwritten notes; implemented document scene CV techniques for table detection and robust processing; deployed distributed document processing on AWS (EC2,S3) and built RESTful APIs for integration with e-discovery systems.

Full Stack Developer at Metadata

February 1, 2019 - September 10, 2025

Integrated real-time performance dashboards; designed automated ETL & preprocessing pipelines for legal/archival datasets using AWS Lambda & SageMaker; conducted load testing; built end-to-end NLP pipelines to automate classification, review, and summarization of legal documents; collaborated with legal teams to ensure model fairness and transparent AI deployment.