AI Deploy Network
Nhat Le (Nate Q)

Nhat Le (Nate Q)

Community

AI Engineer | Production AI Systems | LLM, RAG & Agentic AI | Computer Vision & MLOps · Agentic Workflows · AI Evaluation & Testing · Autonomous Agents · Multi-Agent Systems · RAG Systems

VietnamHa NoiSenior

LinkedIn
Available for Contract Projects Available for Projects
Published Evidence
0
AI Deployments
0
AI Case Studies
community Builder

Measured business impact

Outcomes reported against approved AI implementations, presented for executive review.

Hours Automated
—

Operational hours recovered through verified AI deployments.

Cost Savings
—

Verified operating cost reductions delivered for client organisations.

Revenue Impact
—

Revenue created or protected through deployed AI solutions.

AI Deployments
0

Implementations reviewed and approved against the Evidence Standard.

Published Case Studies
0

Documented implementations with published business outcomes.

CommunityOrganisation Trust SignalsHow this is verified
  • VerificationCommunity
  • SpecialisationAgentic Workflows
  • Client Portfolio3 clients
  • Certifications2
  • Industries Served5
  • Response TimeWithin a few hours
  • AvailabilityAvailable for Contract Projects
  • LocationVietnam
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

Top business outcomes

Reported by the builder on the record that produced them, in the original reporting period. Nothing is annualised or converted.

This builder has not reported measurable business outcomes yet.

Builder-reported outcomes. Not yet reviewed by AI Deploy Network. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.

Capabilities: declared and demonstrated

0 of 35 listed capabilities are supported by an approved deployment or published case study on this platform. The rest are Builder-declared and are not presented as verified.

Implementation capability

  • Agentic Workflows
  • AI Evaluation & Testing
  • Autonomous Agents
  • Multi-Agent Systems
  • RAG Systems

Technologies

  • Agno
  • Amazon ECS
  • 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

Industries

  • Digital Health
  • Health Insurance / Payers
  • E-Commerce
  • Data & Analytics
  • Architecture & Engineering

Evidence

No AI Deployments or Case Studies have been published yet. Declared capabilities below describe what this builder offers; they are not evidence of delivered work.

Client Portfolio

Diverse industry experience

  • Healthcare Provider

    Confidential

    Agentic AI • Healthcare Providers

    Vietnam2025

    An 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

    Confidential

    Computer Vision • Construction

    Iraq2025

    Built 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

    Confidential

    Agentic AI • IT Services & Systems Integration

    United States2025

    The 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 recognition

  • IBM RAG and Agentic AI Professional Certificate

    IBM

    Verified Credential

    Issued May 2026

    Credential ID: VLLFXMG7D8AD

    View Credential

  • IELTS 7.0 (Speaking 7.5, Listening 8.0)

    British Council

    Verified Credential

    Issued Jan 2025

    View Credential

About

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.

Available for remote contract projects, particularly in RAG, AI agents, document AI, backend AI systems and production ML.

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.