Phuc Tran

Full-Stack Product Engineer with 4+ years of hands-on experience bridging the gap between data science and production-ready applications. Specialized in building end-to-end software solutions, interactive dashboards, and scalable data pipelines, powered by machine learning, statistical methods, and real-time data analysis. Proven track record of leading cross-functional teams and aligning product features with growth strategies to deliver maximum business value.

Product Engineer (Data-Driven) at VNG Corporation/Verichains (2022-05 – 2026-01)

• Engineered and deployed end-to-end market monitoring applications by integrating public data with premium on-chain APIs (Nansen, Glassnode) to track real-time capital flows across Ethereum, BSC, and Solana.
• Architected and led the development of interactive custom dashboards using behavioral metrics to visualize accumulation and distribution patterns of large-scale investor groups, enabling daily macro and sector-level sentiment analysis.
• Built automated data processing pipelines to analyze tokenomics events (unlocks, listings, staking), translating complex market hypotheses into actionable "gem" hunting and alpha signals for end-users.
• Drove product strategy to categorize and track capital rotation across emerging narratives (AI, DeFi, RWA...); collaborated with Marketing teams to align dashboard features with user growth and engagement campaigns.
• Led a cross-functional team of full-stack developers and UI/UX designers; managed sprint planning, task allocation, and cross-team alignment to ensure on-time product delivery.

Data Scientist at VNG Corporation/Verichains (2026-01 – 2026-07)

• Engineered AI agents and Retrieval-Augmented Generation (RAG) pipelines using n8n workflows, local LLMs, and prompt engineering to analyze, interpret, and prioritize internal security alerts. Integrated domain-specific knowledge bases to enhance contextual reasoning, automate alert triage, and reduce false-positive cases by 60% compared with traditional rule-based approaches.
• Researched, designed, and independently developed hybrid statistical anomaly detection frameworks for GitLab user activities and server access logs by combining behavioral baselines, statistical scoring, and graph-based analysis. The solution enabled security teams to identify suspicious behaviors 2–4 days earlier than legacy monitoring systems.

AI Engineer at Kobiton (2020-11 – 2022-04)

• Engineered image-based ML models and segmentation techniques to automate mobile testing, focusing on abnormal behavior detection and UI/UX text optimization.
• Architected robust ETL pipelines integrated with PostgreSQL and AWS S3, managing end-to-end model training and evaluation on EC2 infrastructure for scalability.
• Deployed image preprocessing workflows leveraging Google Vision API and custom OCR pipelines, significantly improving reliability in edge-case recognition.
• Optimized model production performance through rigorous post-processing and error analysis, effectively reducing false positives and enhancing feature tracking accuracy.
• Spearheaded technical documentation and cross-functional collaboration to align AI solutions with product goals, ensuring seamless system integration and team scaling.
• Built end-to-end data processing pipelines for anomaly detection, including data exploration, cleaning, feature engineering, and large-scale behavioral analytics to transform raw security events into production-ready machine learning datasets.
• Designed, implemented, and maintained production-ready AI microservices using gRPC, enabling scalable deployment of machine learning models and seamless integration with internal platforms and distributed services.
• Collaborated closely with Data Engineers to identify, assess, and prepare high-quality data sources, optimize ETL workflows, and establish reliable data pipelines that supported scalable AI solution development and deployment.
• Evaluated and proposed high-impact AI initiatives by identifying automation opportunities across internal systems, translating business requirements into technical solutions, and presenting recommendations to cross-functional stakeholders for implementation.
• Owned the end-to-end AI development lifecycle, from data preparation and algorithm research to model development, validation, deployment, technical documentation, and knowledge sharing, ensuring scalable and maintainable AI solutions across multiple internal business domains.

AI Engineer Intern at Quodisys (2020-08 – 2020-11)

• Developed OCR & Document Processing tools to extract structured data from diverse formats, including medical records, school invoices, and administrative forms.

AI Engineer Intern at FPT Telecom (2019-05 – 2019-09)

• Engineered Computer Vision models for TV broadcast logo detection to support automated content monitoring and analytics.

Bachelor of Computer Science in Computer Science – Ton Duc Thang University (2016 – 2020)