AI Deploy Network
Do Quoc Dung

Do Quoc Dung

Verified

Senior AI Engineer · Agentic Workflows · AI Agents · Conversational AI · Multi-Agent Systems · RAG Systems

VietnamHa NoiSenior

LinkedInGitHub
Available for Projects Available for Fractional Roles Available for Full-Time Roles
Published Evidence
1
AI Deployments
0
AI Case Studies
verified 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
1

Implementations reviewed and approved against the Evidence Standard.

Published Case Studies
0

Documented implementations with published business outcomes.

VerifiedOrganisation Trust SignalsHow this is verified
  • VerificationVerified
  • SpecialisationAgentic Workflows
  • AI Deployments1
  • Client Portfolio3 clients
  • Certifications2
  • Industries Served3
  • Response TimeWithin 24 hours
  • 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, recorded on deployment and case study records that AI Deploy Network reviewed before publication. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.

Capabilities: declared and demonstrated

7 of 38 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 Agents
  • Conversational AI
  • Multi-Agent Systems1
  • RAG Systems
  • AI Agent1
  • Internal Business Automation1
  • Workflow Automation1

Technologies

  • Amazon Bedrock
  • Anthropic Console
  • Atlassian Intelligence
  • BGE Embeddings
  • BigQuery
  • Brave Search API
  • Chroma
  • Claude Agent SDK
  • Claude Code
  • Cohere Embed
  • Cohere Rerank
  • DeepEval
  • Dify
  • Elasticsearch Vector
  • FAISS
  • Guardrails AI
  • Hugging Face AutoTrain
  • Jina AI
  • Kubernetes1
  • LangChain
  • LangGraph1
  • Milvus
  • MongoDB Atlas Vector Search
  • Pinecone
  • Qdrant1
  • Redis Vector
  • pgvector

Industries

  • IT Services & Systems Integration
  • Aviation & Airports
  • Data & Analytics

Client Portfolio

Diverse industry experience

  • Global Bank

    Confidential

    Conversational AI & RAG • Aviation & Airports

    Singapore2026

    As an AI Engineer at FPT Software, delivered backend AI capabilities for an airport conversational assistant across web and mobile, supporting flight-related, retail, and personalised user journeys. Built a RAG workflow and real-time FastAPI/WebSocket services deployed on AWS.

  • Global Bank

    Confidential

    LLM Data Enrichment & Entity Resolution • Data & Analytics

    Germany2026

    As an AI Engineer at FPT Software, built and extended a data platform combining structured data enrichment with LLM-based information extraction and schema validation. Developed an entity-resolution pipeline that recovered approximately 5,500 legacy prospects missing from the platform inputs.

  • Global Bank

    Confidential

    Multi-Agent AI Orchestration • Cross-Industry / Horizontal

    South Korea2026

    As an AI Engineer at FPT Software, designed a stateful multi-agent orchestration layer coordinating database NL-to-SQL, IT service management automation, and infrastructure monitoring workflows. The pilot reduced manual IT operations effort by approximately 40%.

Professional Certifications

Some recognition

  • AWS Generative AI Developer Professional

    Amazon Web Services

    Issued Jan 2026 • Expires Jan 2029

  • OCI Generative AI Professional

    Oracle

    Issued Feb 2025

About

AI Engineer focused on taking agentic AI systems from prototype to reliable production deployment. At FPT Software, I build enterprise AI solutions across conversational AI, retrieval-augmented generation, data enrichment, and multi-agent orchestration.

My work includes LangGraph-based workflows coordinating database, IT service management, and infrastructure monitoring agents; FastAPI and WebSocket backends for real-time conversational systems; and AWS deployments using Bedrock, Lambda, EC2, Strands Agents, and Bedrock AgentCore.

I work primarily with Python, FastAPI, LangChain, LangGraph, Pydantic AI, AWS, and vector databases including Qdrant, Pinecone, and Weaviate. I also build retrieval and evaluation workflows, including a Vietnamese healthcare AI project combining a fine-tuned embedding model with an agentic RAG pipeline.

I am most interested in production AI systems where reliability, observability, tool integration, and measurable business value matter.

Ready to discuss your AI implementation?

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