Data & AI - Artificial Intelligence Architect

Data & AI - Artificial Intelligence Architect

CFGI is building its Data & AI practice in Singapore and is seeking a hands-on Artificial Intelligence Architect to own the solution architecture of client artificial intelligence builds and lead the team’s technical design.

As the team’s lead architect and client-facing technical lead, you will design reference architectures — agent-first where open-ended reasoning or tool selection genuinely adds value, and conventional or deterministic designs where those are the better answer — set integration, security, and controls standards, own model and vendor strategy, and guide the engineering team in building solutions that are scalable, governed, and audit-ready.

This is a Manager-level role based in Singapore, working with the Data & AI build team, including artificial intelligence engineers, a data engineer, and data scientists; CFGI’s APAC and global Data & AI, controls, and managed-services practices; and client Chief Technology Officers, Chief Data Officers, enterprise architects, and security and risk leaders.

The role is hybrid in Singapore, with travel across Asia-Pacific expected for client work. Work-authorization sponsorship may be supported where required, subject to applicable eligibility and approval requirements.

Healthcare and life sciences is the priority market, with delivery extending across CFGI’s other priority sectors, including technology and software, real estate and real-estate investment trusts,

and industrials. Private equity will also serve as a cross-cutting channel across sectors.

What you might expect:

Own the solution architecture of the practice’s client artificial intelligence work and lead the team’s technical design from strategy and use-case definition through production deployment.

Translate client strategies and use cases into build-ready reference architectures covering components, integration patterns, data flows, model choices, security controls, governance requirements, and delivery approaches.

Determine when agentic architecture genuinely adds value over conventional or deterministic solutions and establish appropriate design patterns for each.

Design reusable agent-first and enterprise artificial intelligence reference architectures, including multi-agent orchestration, agent-to-agent and Model Context Protocol tool layers, model gateways, retrieval and knowledge architectures, and integration patterns connecting artificial intelligence solutions to the client’s broader enterprise estate.

Own model and vendor strategy and third-party model risk, including the approach to model selection across frontier and open-weight providers.

Evaluate platforms, models, and tooling based on capability, cost, latency, context requirements, data residency, security, portability, and business needs.

Design for model and vendor provenance, contractual controls, portability, and exit so that clients can make defensible technology decisions without unnecessary vendor lock-in.

Embed security and controls into architecture from the outset, including prompt-injection defenses, least-privilege agent identity, tool authorization, sandboxing, human approval gates, data-leak prevention, memory integrity, data sovereignty, auditability, and evidence requirements.

Design architectures capable of satisfying the governance, control, and audit expectations of regulated clients, boards, security teams, and regulators.

Set and maintain the technical standard for the practice, including architecture principles, design patterns, non-functional requirements, technical assurance, and design-review processes across engagements.

Review and assure engineering work while guiding artificial intelligence engineers, the data engineer, and data scientists toward consistent technical and architectural standards.

Make critical build-versus-buy, platform, model-selection, integration, data, and architecture decisions across client engagements.

Serve as the client-facing technical lead and run architecture and design sessions with Chief Technology Officers, Chief Data Officers, enterprise architects, security leaders, risk leaders, and other technical and executive stakeholders.

Translate complex business problems into governed technical solutions and communicate architecture credibly to both technical teams and executive buyers.

Contribute technical solutioning to proposals and pursuits by shaping the technical approach, delivery plan, architecture, and risk position alongside practice leadership.

Build reusable practice assets, including reference architectures, architecture patterns, technical standards, methodologies, and accelerators.

Mentor the engineering team and raise the technical bar as the Data & AI practice grows.

Work with the Partner, Data & AI Innovation and, as the practice grows, the wider CFGI architecture community on enterprise-level technical and architecture decisions.

What you must have:

Approximately 6–10 years of experience in technical delivery, solution architecture, enterprise architecture, data architecture, cloud architecture, artificial intelligence architecture, and/or related technology consulting, including substantial experience architecting production artificial intelligence, data, or platform systems and leading technical work.

Hands-on experience architecting production generative artificial intelligence and/or agentic systems, including orchestration, retrieval or knowledge architectures, model integration, enterprise integration patterns, and the transition of artificial intelligence solutions from design into production.

Experience serving as a client-facing technical lead, translating business requirements into technical architecture and working directly with enterprise architects, technology leaders, security or risk stakeholders, and executive decision-makers.

What sets you apart:

Deep experience designing agentic and generative artificial intelligence systems, including multi-agent orchestration, agent-to-agent patterns, Model Context Protocol, model gateways, retrieval-augmented generation, vector architectures, knowledge graphs, prompt strategy, and system-level evaluation.

Experience with agentic frameworks such as LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, or comparable orchestration technologies.

Strong understanding of frontier and open-weight model ecosystems and experience developing model-selection strategies based on capability, cost, latency, context window, data residency, security, and operational requirements.

Experience evaluating proprietary model families such as Anthropic Claude, OpenAI GPT, and Google Gemini as well as open-weight models such as Meta Llama, Google Gemma, Microsoft Phi, Mistral, Cohere Command, or comparable technologies.

Familiarity with models and artificial intelligence ecosystems relevant to Asia-Pacific markets, including Alibaba Qwen, DeepSeek, Zhipu AI GLM, Moonshot AI Kimi, 01.AI Yi, Naver HyperCLOVA X, LG EXAONE, ELYZA, AI Singapore SEA-LION, or similar regional and sovereign models.

Deep architecture experience with one or more major cloud environments such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform and their managed artificial intelligence and machine-learning services.

Experience with Microsoft Foundry and Azure OpenAI, Amazon Bedrock, Google Vertex AI, or comparable managed artificial intelligence platforms.

Understanding of sovereign and regional cloud environments relevant to Asia-Pacific data-residency requirements, including Alibaba Cloud, Tencent Cloud, Huawei Cloud, Naver Cloud, locally operated global-cloud environments, and/or self-hosted inference architectures.

Experience designing integration architecture across application programming interfaces, event-streaming technologies, identity platforms, enterprise applications, and data environments.

Working knowledge of modern dat