Regulatory intelligence · 2026
Monitorscape
A production pipeline that turns fragmented public information into structured material analysts can review.
- My role
- Backend engineering, data pipelines, scraping, document processing, AI-assisted analysis
Engineering portfolio
Amin Haiqal.
Software engineer.
I build software, develop task-specific AI and automate workflows, from the initial requirements through to a working system.
Based in Kuala Lumpur. Independent engineering through Axelyn. About me
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Astro
Next.js
Go
Python
PyTorch
Hugging Face
scikit-learn
n8n
OpenRouter
Docker
Kubernetes
AWS
Google Cloud
Selected project
Selected engineering work
Real tasks.
Working systems.
Projects spanning data infrastructure, business operations and event technology. Each case study records my responsibilities, the system decisions and the evidence available.
E-commerce operations · 2025
Pastelocity
A custom business system connecting products, customers, orders, payments, receipts and daily administration.
- My role
- Product development, backend services, integrations and web implementation
Event technology · 2026
Memora
A private QR-based experience for collecting guest photos and messages while an event is happening.
- My role
- End-to-end product development and production infrastructure
The engineer behind the work
Practical delivery.
Continued investigation.
I work through Axelyn, a software engineering, AI and automation practice in Malaysia, with suitable clients internationally.
From the first workflow
to a working release.
I lead delivery directly, from implementation planning to backend engineering, deployment and handover. My experience spans backend services, data pipelines, API integration, production systems and applied AI.
My technical focus is systems and AI infrastructure: execution boundaries, state, observability and recovery. Client delivery supports the practice and continued investigation of distributed systems and reliable AI workloads.
Discuss a projectOperating principles
Clear decisions.
Accountable engineering.
The work needs more than a finished interface. These principles guide the implementation and the responsibilities around it.
- Define the work
- Agree on one problem, the required access and a result that can be checked.
- Keep evidence close
- Connect derived results to their sources and make unresolved issues visible.
- Respect human authority
- Prepare information and hand off decisions that require judgement or permission.
- Leave clear ownership
- Document how the system runs, what can fail and who is responsible after handover.
Engineering / Axelyn Forge
The system
around the model.
I built Forge to turn verified career evidence into tailored documents while keeping facts, layout and publication under application control.
Working private system · Public blueprint
Canonical data, evidence and templates define what may change.
- Verified evidenceCandidate facts & sources
- Model proposalConstrained rewrite operations
- ValidationDeterministic checks & rendering
- PublicationComplete output package
Operational controls
Profile isolation · Serialized private jobs · Usage ledger · Health-checked releases
The application validates model proposals and publishes complete artifact packages. The documented workflow runs as a private service, with serialized jobs and external model APIs.
The public blueprint explains typed model operations, deterministic validation, isolated state, usage records and deployment recovery. The implementation and private data remain private.
Distributed systems and AI workload infrastructure guide my ongoing investigation. Further results need their own implementations and reproducible evidence.