AI Architect

3 CUBED BUSINESS CONSULTING PTE. LTD.; Singapore; On-site; SGD 200,000 - 320,000; Full time

Job summary

3 CUBED BUSINESS CONSULTING PTE. LTD. seeks an expert to architect and lead enterprise AI, GenAI, and Agentic AI initiatives.

You will design end-to-end AI architectures spanning data ingestion, governance, AI/ML, models, and deployment, ensuring secure, scalable production-ready solutions. Strong leadership across AI/ML, Data Engineering and Cloud is essential. The role emphasizes cloud-native design, guardrails, observability, and production-readiness, with a focus on scalable architectures

Qualifications

• 10+ years in software, data or cloud architecture with AI/GenAI focus.
• Proven track record taking enterprise AI/GenAI solutions from architecture to production.
• AWS architecture/AI certifications preferred; Azure AI certifications advantageous.

Skills

Enterprise AI architecture

AWS cloud architecture

GenAI / AI use cases

Databricks / Lakehouse

AI governance & guardrails

LLMOps / monitoring

Tools

Amazon Bedrock

OpenSearch

Azure AI

Job description

Role Summary

Architect and lead enterprise-scale AI, GenAI and Agentic AI solutions across Knowledge Management, Data Engineering, Data Management and Application Development use cases, from solution design and PoC/MVP through implementation to production . Design enterprise AI solutions and platforms integrating structured and unstructured data, including intelligent search, RAG, AI assistants, intelligent document processing, data intelligence and AI-enabled automation. Define end-to-end AI architecture covering data ingestion and engineering → data management/governance → AI/ML and GenAI → models/agents → integration → guardrails → deployment and observability , leveraging cloud and modern data/AI platforms such as AWS, Azure . Identify and architect AI-enabled use cases across the data and application lifecycle , including data discovery, data quality, metadata management, engineering automation, code generation/testing, application modernisation and operational support. Provide technical leadership across AI/ML, Data Engineering, Data Management and Cloud , translating business requirements into scalable, secure and production-ready AI solutions.

Key Experience & Skills Required

• Strong hands‑on experience architecting enterprise AI, GenAI, RAG and Agentic AI solutions , including LLMs, embeddings, hybrid/vector search, grounding, multi-agent orchestration, tool integration and model routing.
• Strong AWS AI/cloud architecture experience , particularly with Amazon Bedrock, AgentCore, Knowledge Bases, Guardrails, OpenSearch and core AWS data/integration services ; experience with equivalent Azure AI services and the ability to architect cloud-agnostic solutions are highly desirable.
• Experience with modern data/AI platforms such as Databricks , covering Lakehouse architecture, data engineering, data pipelines, data quality and governance, AI/ML workloads, and integration of enterprise data with GenAI and Agentic AI solutions.
• Experience architecting and implementing AI use cases across Knowledge Management, Data Engineering, Data Management and ADLC , such as intelligent search and knowledge discovery, metadata enrichment, data quality automation, engineering productivity, code/test generation and AI-driven operational automation.
• Proven experience designing production-grade, secure and governed AI solutions , including IAM/RBAC, Responsible AI, guardrails, PII/data protection, human-in-the-loop and AI/LLMOps, while balancing accuracy, scalability, performance, security, cost and maintainability .
• Experience in AI/RAG evaluation and observability , defining and monitoring evaluation metrics such as Recall, Precision, Faithfulness, Completeness, Relevance and Answer Correctness , along with LLM/agent performance, latency, token usage and operational monitoring.

Experience & Qualifications

• 10+ years of overall experience in software, data or cloud architecture, with substantial recent experience in AI/ML and GenAI solution architecture .
• Proven experience taking at least one enterprise AI/GenAI solution from architecture through production , beyond PoC or experimentation.
• AWS architecture/AI certification preferred ; Azure AI Architecture certifications would be an added advantage.
• Experience working in large-scale enterprise or regulated environments with strong security, governance and auditability requirements is desirable