Malaysia Digital Economy Corporation (MDEC) Hiring! Full Time Technical Quality Assurance Manager in Selangor - Ricebowl

JOB PURPOSE

Owns the technical quality function across the entire GII portfolio. Acts as the gatekeeper for ecosystem submissions into the GII Digital Lab / Sandbox, manages the Digital Lab as an operating environment, and ensures consistent quality reporting for all 10 pilots through to pilot-stage delivery

DUTIES AND RESPONSIBILITIES

Mandate 1 — Quality Oversight of All 10 Pilots

• Define and own the GII Quality Assurance Framework — covering AI model validation, data-quality checks, code-quality standards, security review, performance benchmarking and pilot-acceptance criteria.
• Embed QA discipline into each Technical Delivery Manager's pilot — review architecture proposals, validate test plans, sign off on milestone deliverables.
• Conduct end-to-end QA on each pilot before pilot deployment — functional testing, AI model behaviour validation, data-pipeline integrity, security and privacy checks.
• Maintain the GII Quality Dashboard — track defect density, AI model performance, data quality, security findings, and acceptance status across all 10 pilots.
• Produce monthly Quality Reports for the Head of GII, Assistant Head of GII and Steering Committee — including pass/fail status, defect summaries, model-drift indicators and remediation actions.
• Coordinate with the Solution Architect (Faizullah) on architectural quality and with the Legal Expert (Hani) on compliance and data-protection sign-off.

Mandate 2 — Assessing Ecosystem Submissions to the Digital Lab

• Act as the technical gatekeeper for proposals submitted by the ecosystem (startups, SIs, hyperscalers) to the GII Digital Lab — assess proposed solutions against feasibility, fit-to-problem and quality criteria.
• Run the technical-vetting workflow for submissions: review proposal architecture, evaluate AI/data approach, assess vendor track record, validate proof-of-concept claims.
• Score submissions against the GII Rubric (in coordination with NAIO and the Problem Statement & Impact Data Analyst).
• Conduct technical due diligence on shortlisted vendors before they enter the Digital Lab — including security posture, data-handling practices and code-quality standards.
• Recommend acceptance, rejection or revision of submissions to the Steering Committee.
• Maintain the Submission Pipeline Tracker — pending submissions, in-vetting, accepted-to-lab, rejected, with reasons documented.

Mandate 3 — Managing the GII Digital Lab / Sandbox

• Own day-to-day operations of the GII Digital Lab — the sandbox environment where vendors build and test their MVPs before pilot deployment.
• Manage Digital Lab infrastructure — cloud environments (via CFA / hyperscaler credits), development tooling, data sandboxes, security boundaries.
• Onboard new vendors into the Digital Lab — provision accounts, access controls, sandbox data, integration endpoints.
• Run the Digital Lab as a controlled environment for vendor builds — enforce coding standards, security guardrails, data-handling protocols, and the GII Quality Framework.
• Manage the build pipeline in the Digital Lab — from initial setup through development, internal testing, and graduation to pilot deployment.
• Coordinate with the Digital Platform & Product Executive (************* portal) on integration touchpoints.
• Maintain the Digital Lab Operations Handbook — SOPs, access protocols, security policies, escalation paths.
• Report Digital Lab

Required Skillsets

• Senior QA and software-engineering, AI & Data background - preferably with experience leading QA functions in technology or innovation environments.
• Deep AI/ML literacy - model validation, model-drift detection, bias and fairness testing, AI safety review. The role title explicitly calls out AI & Data.
• Data engineering and data-quality expertise - schema validation, data-pipeline integrity, data-protection compliance.
• Cloud-platform engineering - AWS / Microsoft Azure / Google Cloud sandbox management, infrastructure-as-code, access controls.
• Security and privacy fundamentals - OWASP, secure-development practices, PDPA Malaysia, data-handling protocols.
• Vendor management and submission-vetting experience — comfortable assessing third-party proposals against technical criteria.
• Strong reporting discipline - ability to translate technical QA findings into executive-level dashboards and decision-ready reports.
• Comfortable owning a sandbox / lab environment as a managed product — uptime, access, performance, vendor experience

Key Deliverables

• GII Quality Assurance Framework (Q*************)
• GII Quality Dashboard, live by Q*************
• Digital Lab Operations Handbook (Q*************)
• Monthly Quality Reports to Head of GII + Steering Committee
• End-to-end QA sign-off on all 10 pilots before pilot deployment

QUALIFICATIONS

Academy and Professional Qualifications:

• Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence, Machine Learning, Data Engineering, Cybersecurity, Information Systems, or related technical disciplines from a recognised institution.
• Master's Degree in Artificial Intelligence, Data Science, Computer Science, Cybersecurity, Software Engineering, Technology Management, or related fields would be highly advantageous.
• Professional certifications in Cloud Platforms, AI/ML, Data Engineering, Quality Assurance, Cybersecurity, or Software Architecture are highly desirable.

Preferred Certifications

• AWS Certified Solutions Architect / Machine Learning Specialty
• Microsoft Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• Certified Software Quality Engineer (CSQE)
• Certified Information Systems Security Professional (CISSP)
• Certified Cloud Security Professional (CCSP)
• TOGAF Foundation or equivalent architecture certification
• Certified Scrum Master (CSM), Professional Scrum Master (PSM), or SAFe certification.

Professional Experience:

• Minimum 8–10 years of relevant working experience in software engineering, quality assurance, AI/ML engineering, data engineering, solution architecture, cloud engineering, or technical governance roles.
• Proven experience establishing and managing enterprise-level Quality Assurance frameworks, testing standards, governance models, and technical acceptance processes.
• Demonstrated experience managing quality assurance activities across multiple concurrent technology projects, products, or digital transformation initiatives.
• Strong experience assessing AI/ML models, including model validation, model performance evaluation, bias detection, explainability assessment, and model monitoring.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud, including sandbox, development, testing, and production environments.

COMPETENCIES

• Strong Analytical & Critical Thinking
• Risk Awareness & Decision-Making
• Problem-Solving Excellence
• AI Model Validation
• Model Drift Detection & Monitoring
• Machine Learning Quality Assurance
• Enterprise Quality Assurance Management
• Test Strategy Development
• Functional & Non-Functional Testing
• Solution Architecture Review
• API & Integration Architecture
• Cloud Engineering (AWS, Azure, GCP)
• Technical Due Diligence
• Technology Risk Assessment
• Quality Reporting & Dashboard Development