Ownership-minded
Takes early-stage backend, data, and cloud scope beyond boilerplate and follows it through delivery.
Software Engineer · Backend, Cloud & Applied AI · Malaysia

Engineering focus
Software engineer with hands-on experience building Python/FastAPI backends, automating Terraform-managed AWS delivery and CI/CD, and developing reliable applied-AI systems with graph retrieval and structured validation. Experienced productionizing early-stage systems for stronger traceability, testability and reliability. AWS Certified Machine Learning Engineer – Associate.
I’m a software engineer based in Bangi, Selangor, with a backend foundation in Python, FastAPI, and PostgreSQL, hands-on Terraform-managed AWS delivery ownership, and an emerging specialization in reliable applied-AI systems.
At AMIC, I own the backend, database, and AWS delivery scope for SAFAPAC and contribute to AIRIS through AI workload testing, bottleneck analysis, reporting, and optimization recommendations for senior developer/project manager review. Outside work, I’m actively developing AnotherEdenAI around typed graph retrieval, deterministic candidate generation, structured validation, and controlled LLM refinement.
I hold three cloud and AI credentials and am open to Backend Software Engineer, Cloud & DevOps Engineer, and Applied AI Engineer opportunities across Malaysia from November 2026.
Takes early-stage backend, data, and cloud scope beyond boilerplate and follows it through delivery.
Strengthens data models, calculation traceability, validation, testing, and operational documentation.
Works across domain research, frontend delivery, senior engineering, and stakeholder training.
Aerospace Malaysia Innovation Centre in collaboration with Airbus · November 2025 – October 2026
Rebuilding FastAPI/PostgreSQL backend and calculation workflows while owning Terraform-managed AWS staging/production delivery and containerized CI/CD.
Personal project · December 2025 – Present
Building a graph-backed AI system that generates deterministic candidates before constrained LLM refinement, structured validation, correction, and fallback.
Supporting contribution
Testing AI workloads under concurrent demand, analyzing bottlenecks, and translating findings into optimization recommendations for senior engineering review.
AIRIS · Supporting contribution
A compact three-stage AIRIS contribution path shows controlled workload testing, bottleneck diagnosis with optimization research, and an engineering handoff with senior-engineer acceptance and stakeholder training as outcomes. No resulting production improvement is claimed.
Aerospace Malaysia Innovation Centre
Nov 2025 – Oct 2026
PETRONAS Digital Sdn Bhd
Jan 2024 – Aug 2024
Machine learning engineering on AWS.
Credential ID7e465217-d4bb-4080-a529-259747407fa3Foundational AWS cloud knowledge.
Credential IDb8964358-719b-4a4d-81c2-a0e1e6602550Foundational AI concepts and Azure services.
Credential ID255781426dc44f9cUniversiti Teknologi PETRONAS
CGPA: 3.51