Agentic AI Architect Generative AI Architect

A major US multinational is expanding its Data & AI consulting practice out of Singapore. They are hiring a practitioner-architect to be the technical owner for all client AI deliveries.

You will be the most senior hands-on architect in the team and the go-to technical face for clients. Your job is to decide how we build - when to go agent-first and when to keep it simple and deterministic - and to make sure what we build is secure governed production-ready and defensible to risk security and audit teams.

You will work with a small build team in Singapore AI Engineers Data Engineer Data Scientists partner with global Data & AI and Controls teams and sit across the table from CTOs CDOs Enterprise Architects and CISOs/CROs.

What Youll Do

1. Lead Architecture for Client Builds
Define the target architecture from problem framing to live production. Convert business use-cases into clear blueprints - components data flow model choices integration approach security and governance. Create reusable reference architectures for both enterprise AI and agentic systems - multi-agent workflows A2A and MCP tool layers gateway patterns RAG / knowledge systems and enterprise integration. Set the standard for when agentic actually helps vs when it doesnt.

2. Own Platform Model & Risk Decisions
Own the model/vendor strategy. Evaluate and compare frontier closed models and open-weight models on performance cost latency context residency security and portability. Build evaluation criteria selection guardrails and exit strategies so clients dont get locked in. You own the build-vs-buy call.

3. Bake in Security Controls & Governance
Design with controls on day zero - injection safeguards least-privilege agent identities tool permissioning sandboxing human-in-the-loop DLP memory integrity sovereignty audit trails and evidence. Make sure designs can pass muster with regulated clients boards and regulators across APAC.

4. Set the Technical Bar for the Practice
Define architecture principles patterns NFRs and design review / assurance rituals. Review engineering work mentor the team and raise overall quality. Partner with practice leadership to shape proposals - technical approach delivery plan risk stance - and build reusable assets accelerators and methods.

What Were Looking For

• 6-10 years in solution / enterprise / data / cloud / AI architecture or consulting with real experience shipping production AI / data / platform systems.
• Proven track record building production GenAI and agentic systems end-to-end - orchestration retrieval model integration enterprise systems and moving from POC to prod.
• Comfortable as the client-facing technical lead - running architecture workshops with senior enterprise stakeholders.

Strong Advantage If You Have:

• Deep agentic expertise - multi-agent orchestration A2A MCP gateways RAG vector stores knowledge graphs evals.
• Hands-on with frameworks like LangGraph CrewAI AutoGen Microsoft Agent Framework or similar.
• Good understanding of model landscape - Anthropic / OpenAI / Gemini Llama / Gemma / Phi / Mistral / Cohere and how to pick between them.
• Exposure to APAC models - Qwen DeepSeek GLM Kimi Yi HyperCLOVA X EXAONE ELYZA SEA-LION etc.
• Strong on cloud AI stacks - Azure Foundry / AOAI AWS Bedrock GCP Vertex - plus knowledge of sovereign clouds for residency - Alibaba Tencent Huawei Naver self-hosted inference.
• Solid integration design - APIs events identity enterprise apps data platforms - plus MLOps / Lakehouse / observability.
• Security for AI systems - injection defense tool auth identity approvals audit privacy.