Dylan | AI Builder | AI Deploy Network

About Dylan

Senior Java Engineer 4 years + AI Engineer.

Flezi FPT Level 3 (CASAN Level).

Professional profile

Location: Ho Chi Minh, Vietnam
Time zone: Asia/Bangkok
Work mode: Remote, Hybrid, On-site
Availability: Available Now
AI experience: 1+ years
Industry experience: 4+ years
Languages: English, Vietnamese
Working hours: 9:00-18:00 GMT Mon-Fri
Typical response: Within a few hours

Proven on the network

Implementation records and publicly available work, where published.

1 AI Deployment delivered, including 1 publicly identified implementation available to inspect.

Reported business outcomes

Hours Automated176 hours
period not specified
Automated 176 hours (period not specified)
Cost SavingsUSD 100,000
period not specified
Reduced operating costs by USD 100,000 (period not specified)

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.

What Dylan builds

Implementation domains listed on this profile. Evidence-supported items are identified separately below.

Specialisations

AI Agents

AI Analytics

RAG Systems

Semantic Search

Voice AI

Builder-listed deployment types

AI Analytics
Knowledge Assistant
Process Automation
RAG System
Task Automation
Workflow Automation

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

AI Strategy for ComfortDelGro

Integrated AI to CDG Zig projects, microservices for Back-End.

176 hours automated / per month · SGD 100K cost savings / per month

Automotive · AI Analytics · Knowledge Assistant · Process Automation · RAG System · Task Automation · Workflow Automation

View Deployment

Implementation capabilities

Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.

Evidence-supported

AI Analytics1 record
Knowledge Assistant1 record
Process Automation1 record
RAG System1 record
Task Automation1 record
Workflow Automation1 record

Builder declared

AI Agents
RAG Systems
Semantic Search
Voice AI

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

Claude Fable 5.11 record
Java1 record
Model Context Protocol1 record

Builder declared

AWS Bedrock Guardrails
Amazon ECS
Amazon EKS
Amazon Kinesis Data Streams
Apache Kafka
Argo CD
Confluent
Dart
FastAPI
GitLab CI/CD
Hugging Face Inference
Hugging Face Transformers
Kubernetes
LangChain
LangGraph
Llama 3
MongoDB
Neo4j
Perplexity Sonar
Pinecone
PostgreSQL
Postman
Redis
Redis Vector
Sonar
Stable Diffusion
Supabase Vector
Terraform

Industry experience

Published Deployments support the listed counts. Other industries are Builder declared.

Automotive

1 published Deployment

E-Commerce

Builder declared

Gaming

Builder declared

Media & Entertainment

Builder declared

Retail

Builder declared

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

Community

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 experience

ComfortDelGro
AI Strategy • Automotive
Singapore2026
Implement skills for AI in microservices. Deliver guideline to using MCP connecting Confluent, Jira and Bitbucket. Input resources knowledge file for each services (like an AGENT.md, but actually more than that) for Agent AI can not only strictly follow the project's scope but also deliver the best practices for each solution from each tickets, enhancements, bug fixes, change requirements.
1 AI Deployment
Hours
176
Cost
Currency not specified
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Professional Certifications

Some recognition

FPT Flezi E3
FPT
Issued Sep 2026

Ready to discuss your AI implementation?

Tell Dylan 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.