#EG Data & AI Architect
HybridSingapore, Singapore
Job Summary
Design cloud-native data platforms and pipelines for ingestion, transformation, analytics, and visualization while ensuring scalability, resilience, and security across hybrid environments. Lead the design of AI and agentic architectures combining large language models, reasoning engines, and frameworks like LangChain and RAG to operationalize next-generation systems using hyperscaler ecosystems. Oversee end-to-end architecture quality, provide technical leadership on solution integration and cost optimization, and collaborate with engineers to embed AI capabilities into enterprise platforms. Develop reusable blueprints and reference architectures to accelerate adoption while mentoring teams to uplift engineering capabilities. Partner with clients and technology partners to translate business needs into scalable solutions and represent the organization in technology forums.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or related fields
- 8–12 years of experience in technology, including at least 5 years in data, AI, or solution architecture roles
- Strong hands-on experience in one or more cloud ecosystems (Google Cloud, AWS, Azure)
- Expertise in data engineering frameworks (Databricks, Spark, Kafka, Airflow, DBT) and modern AI architectures (LLM, RAG, agentic systems)
- Familiarity with vector databases (FAISS, Qdrant, Weaviate, Chroma) and MLOps / AIOps pipelines
- Excellent stakeholder communication skills — able to articulate technical strategy in business terms
Desired Qualifications
- Google Professional Data Engineer / Machine Learning Engineer
- AWS Certified Machine Learning or Data Analytics Specialty
- Microsoft Azure AI Engineer Associate
- Databricks Certified Data Architect / Machine Learning Professional
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