AWS Technical Architect- AI Solutions

ITCAN PTE. LIMITED; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

ITCAN PTE. LIMITED seeks an experienced Enterprise AI Architect to lead GenAI initiatives across knowledge management, data engineering and application development. You will design scalable AI platforms, from PoC/MVP to production, integrating data governance, guardrails and observability.

Applicants should have 10+ years in software/data/cloud architecture with hands-on AWS/Azure AI experience, and a track record of delivering regulated, secure AI solutions.

Qualifications

• 10+ years in software, data or cloud architecture with substantial AI/GenAI experience.
• Proven experience taking enterprise AI/GenAI solutions from architecture to production.
• AWS AI/Cloud certification preferred; Azure AI Architecture cert is a plus.
• Experience in regulated environments with strong security, governance and auditability.

Responsibilities

• Architect and lead enterprise AI/GenAI across Knowledge Management, Data Engineering, Data Management and application development.
• Design end-to-end AI platforms from data ingestion to deployment and observability.
• Translate business requirements into scalable, secure AI solutions with governance.
• Drive guardrails, IAM/RBAC, data protection and responsible AI practices.

Skills

Enterprise AI architecture

GenAI

RAG

Agentic AI

LLMs

Vector search

Multi-agent orchestration

Model routing

Cloud architecture

Databricks

OpenSearch

Bedrock

Guardrails

Data governance

IAM/RBAC

LLMOps

Tools

Databricks

OpenSearch

Bedrock

Azure AI Services

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