Data and AI Architect
Summary
Designs and governs enterprise data architecture, AI/ML solutions, and cloud-native platforms to enable scalable analytics and AI-driven decision-making.
Kuala Lumpur, Malaysia | Posted on 05/12/2026
The Data & AI Architect is responsible for defining, designing, and governing the organization’s data architecture,analytics platforms, and AI/ML solutions. This role ensures scalable,secure, and business‑aligned data and AI ecosystems that enable advancedanalytics, machine learning, and AI-driven decision‑making.
The architect partners closely with business leaders, dataengineers, AI engineers, security, and cloud teams to translate strategy intorobust technical solutions.
Key Responsibilities
- Defineand maintain enterprise data architecture and reference models
- Designscalable data platforms including data lakes, data warehouses, and lake-house architectures
- Selectand govern data storage, processing, and analytics technologies
AI & Advanced Analytics Architecture
- Designend-to-end AI/ML solution architectures, from data ingestion tomodel deployment
- Supportuse cases such as predictive analytics, NLP, computer vision, andgenerative AI
- Definemodel lifecycle, MLOps, and AI platform standards
- Ensureexplain monitoring, and reliability of AI solutions
Cloud & Technology Enablement
- Architectdata and AI solutions on cloud platforms (Azure, AWS, or GCP)
- Leverageservices such as:
- Analytics& big data platforms
- AI/MLand GenAI services
- Ensurecost optimization, scalability, performance, and resilience
Governance, Security & Compliance
- Define data governance, metadata management, lineage, and qualityframeworks
- Ensurecompliance with data privacy, security, and regulatory requirements
- Implementsecurity controls including access management, encryption, and auditing
- SupportResponsible AI and ethical AI practices
Stakeholder Collaboration & Leadership
- Translatebusiness requirements into technical data and AI architectures
- Providearchitectural guidance to delivery teams and review solution designs
- Definestandards, best practices, and roadmaps for data and AI initiatives
- Mentorarchitects, data engineers, and AI engineers
Requirements
Required Qualifications & Experience
- Bachelor’sor Master’s degree in Computer Science, Data Science, Engineering, orrelated field
- 8–12+years of experience in data architecture, analytics, or enterprisearchitecture roles
- Strongexperience designing and implementing:
- Datalakes, warehouses, and lake-house solutions
- ETL/ELTand streaming architectures
- Solidknowledge of AI/ML concepts, model lifecycle, and deploymentpatterns
- Experiencewith cloud-native data & AI platforms
- Strongunderstanding of data modeling, metadata, quality, and governance
Technical Skills
- DataPlatforms: SQL & NoSQL databases, data lakes, big data processing
- Cloud:Azure / AWS / GCP data and AI services
- Security& Governance: IAM, encryption, privacy, compliance
Preferred / Added Advantage
- Experiencewith Generative AI and LLM-based solutions
- Knowledgeof Responsible AI frameworks
- Industryexperience in regulated or enterprise‑scale environments
- Strongstrategic and analytical thinking
- Abilityto balance innovation with governance and risk
- Excellentcommunication with technical and non‑technical stakeholders
- Influencingand leadership capabilities
What We Offer
- Opportunityto shape enterprise data & AI strategy
- Exposureto advanced analytics and AI initiatives
- Competitivecompensation and benefits
- Continuouslearning and certification support