Architecting automated ML workflows and production-ready AI solutions with expertise in MLOps, Computer Vision, and NLP
Machine Learning Engineer with 4 years of experience in AI production and delivery, specializing in MLOps, Computer Vision, and NLP. Excel at aligning ML solutions with strategic business objectives, tackling challenging problems under pressure, delivering quick yet robust solutions, and adopting cutting-edge AI technology. Led teams of up to 8 engineers, architected automated ML workflows using Kubeflow and MLflow, and achieved measurable improvements in operational efficiency through MLOps best practices.
Currently working at Success Software Services as Senior Machine Learning Engineer, where I lead system design and implementation for cutting-edge AI solutions including multi-turn conversational AI, MLOps microservices, and large-scale multi-camera systems. Previously led multiple AI teams of 2-8 engineers at FPT Software, establishing technical standards and improving team productivity by 30%.
Real-time AI System with Auto-Labeling, 85% Accuracy, and Edge Deployment
Speech-to-Text & NLP achieving 90% F1-Score with MLOps Pipeline
Centralized Infrastructure serving 5+ AI Teams with Enterprise Security
Multi-Turn Chatbot with Context Management handling 1000+ Daily Inquiries
Real-time Customer Behavior Monitoring across 10+ Locations with Active Learning
Advanced OCR & Segmentation for Japanese Blueprints achieving 94% Accuracy
AI-Powered Thank-You Messages with Sub-Second Response and Prompt Engineering
End-to-End Workflow with Hyperparameter Optimization reducing Manual Steps by 40%
mT5-Based Multilingual Model for Japanese-English-Vietnamese with Real-time Translation
Malay-English ASR System using wav2vec2-XLSR for Code-Switching
AWS Contact Center with Lex achieving 70% Automation and 24/7 Availability
Data Fusion Pipeline combining Video, Audio, Biometrics achieving 85% Accuracy
Multi-Agent LLM System processing 10,000+ Invoices Daily with 95% Accuracy
Thoroughly understand the business requirements and define clear success metrics with stakeholders.
Collect, clean, and prepare high-quality datasets. Implement robust data pipelines to ensure consistent flow.
Design and experiment with various model architectures to find the optimal solution for the problem.
Build robust CI/CD pipelines, monitoring systems, and deployment strategies for production-ready AI.
Measure performance, gather feedback, and iteratively enhance the solution to deliver ongoing value.
My methodology integrates MLOps best practices with business value. I specialize in architecting automated ML workflows using Kubeflow and MLflow, building robust CI/CD pipelines, and implementing monitoring systems that ensure reliable production deployments. This approach delivers not only high-performing models but also sustainable, scalable AI systems that align with organizational goals and accelerate time-to-market.
Discuss Your ProjectSpecialized in MLOps, Computer Vision, NLP, and Generative AI with focus on automated ML workflows and production deployment
Ranked 5th in the nationwide competition focused on ML pipeline development, model training optimization, and building resilient model serving APIs.
Silver Medal (Rank 199/6430) in the ICR - Identifying Age-Related Conditions competition, using machine learning to predict medical conditions.
Ranked 1st in this competitive event organized by the Faculty of Information Technology, showcasing expertise in mathematics, computer science, and related domains.
Linux Foundation
Linux Foundation
Udacity
Udacity
Udacity
Udacity
Udacity
Coursera - Stanford University
Feel free to reach out for collaboration, opportunities, or just to say hello. I'm always open to discussing new projects and ideas.
nam.nd.00@gmail.com
+84.39.39.97.468