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About Nhat
AI Engineer with deep experience building production AI systems across GenAI, RAG, computer vision and document intelligence. I take AI projects end-to-end - from model and retrieval design to FastAPI services, deployment, MLOps and inference optimisation.
I help startups turn messy, AI-built MVPs into production-ready products. I use my own AI harness to refactor codebases, improve scalability, and add production-grade AI features.
Read more
Available for remote contract projects, particularly in RAG, AI agents, document AI, backend AI systems and production ML.
Professional profile
- Location
- Ha Noi, Vietnam
- Time zone
- Asia/Saigon
- Work mode
- Remote
- Availability
- Available for Contract Projects
- AI experience
- 3+ years
- Industry experience
- 3+ years
- Languages
- English, Vietnamese
- Working hours
- 19:00-00:00, Mon-Fri; flexible on Sat–Sun
- Typical response
- Within a few hours
- Available in
- United States, United Kingdom, Australia, Singapore, Vietnam, New Zealand, India
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Proven on the network
Implementation records and publicly available work, where published.
0 AI Deployments delivered. No publicly identified implementations are available to inspect.
03
What Nhat builds
Implementation domains listed on this profile. Evidence-supported items are identified separately below.
Specialisations
Agentic Workflows
AI Evaluation & Testing
Autonomous Agents
Multi-Agent Systems
RAG Systems
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AI Deployments
Real AI implementations demonstrated through evidence.
No publicly identified Deployments are available to inspect.
05
Implementation capabilities
Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.
Evidence-supported
No published evidence linked yet.
Builder declared
- Agentic Workflows
- AI Evaluation & Testing
- Autonomous Agents
- Multi-Agent Systems
- RAG Systems
06
Technology
Technologies listed by the Builder, distinguished by published evidence.
Evidence-supported
No published evidence linked yet.
Builder declared
- Agno
- Amazon ECS
- Anthropic Claude
- Apache Kafka
- Docker
- FastAPI
- Hugging Face Transformers
- JavaScript
- LangChain
- LangGraph
- MLflow
- Model Context Protocol
- NVIDIA Dynamo-Triton
- OpenAI Platform
- OpenCV
- PostgreSQL
- PyTorch
- Qdrant
- React
- Redis Vector
- Supabase Vector
- TensorFlow
- TypeScript
- Zendesk
- n8n
- vLLM
07
Industry experience
Published Deployments support the listed counts. Other industries are Builder declared.
Architecture & Engineering
Builder declared
Data & Analytics
Builder declared
Digital Health
Builder declared
E-Commerce
Builder declared
Health Insurance / Payers
Builder declared
08
Trust & verification
A closer look at how this profile and its evidence are reviewed.
Community Member
Published evidence is reviewed against the AI Deploy Network Evidence Standard.
How verification works
Verified Builder
Independently reviewed by AI Deploy Labs against the platform Evidence Standard.
How this is verifiedWhat the platform has established, and what remains builder-declared.
- Identity and organisation
- Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
- AI Deployments
- An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
- Business outcomes
- Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
- Evidence
- Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
- Currency
- Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
- Traceability
- Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.
The platform does not independently audit client systems or financial records. How this is verified
Professional record and further evidence
Client Portfolio
Diverse industry experienceHealthcare Provider
ConfidentialAgentic AI • Healthcare Providers
Vietnam2025An AI-powered platform for beauty clinics, featuring a multi-agent customer support chatbot, image retrieval system, and virtual cosmetic simulation capabilities. The platform combines LLM-powered agents, retrieval-augmented generation (RAG), and computer vision technologies to enhance customer consultation experiences and clinic operations
Technology Company
ConfidentialComputer Vision • Construction
Iraq2025Built a scalable multi-camera object detection platform for high-volume image processing. Designed asynchronous inference using FastAPI, Kafka, Triton and TensorRT, with Label Studio and MLflow supporting continuous model improvement across 100+ object classes. Achieved 0.56 mAP and reduced end-to-end inference latency by 30%.
Technology Company
ConfidentialAgentic AI • IT Services & Systems Integration
United States2025The platform enables users to transform URLs, documents, spreadsheets, presentations, images, and PDFs into structured knowledge and actionable outputs. By leveraging agentic AI agents, MCP integrations, and multimodal document understanding, the system automatically generates reports, presentations, spreadsheets, and visual assets with minimal human effort
Professional Certifications
Some recognitionIBM RAG and Agentic AI Professional Certificate
IBM
Verified CredentialIssued May 2026
Credential ID: VLLFXMG7D8AD
IELTS 7.0 (Speaking 7.5, Listening 8.0)
British Council
Verified CredentialIssued Jan 2025
Ready to discuss your AI implementation?
Tell Nhat Le (Nate Q) what you are trying to automate and the outcome you need. Your message goes directly to the builder, with a copy kept in your Organisation Workspace.