Data & AI - Artificial Intelligence Engineer
CFGI is standing up its Data & AI build team in Singapore and is seeking a hands-on Artificial Intelligence Engineer who both builds client solutions and operates them in production.
This is a build-and-run role: you will design and build agentic and generative artificial intelligence solutions and own the machine-learning-operations layer — deployment, monitoring, evaluation, and the run-state — that keeps them reliable, controlled, and audit-ready once they are live.
“Full-stack” in this role means end-to-end across the artificial intelligence delivery lifecycle — from building the solution, through deployment, to operating it reliably in production — rather than front-end web development.
This is a Manager-level role based in Singapore, working with the founding Data & AI build team, including the data engineer, data scientists, and, as the team grows, the Artificial Intelligence Architect; CFGI’s APAC and global Data & AI and controls practices; and client technology, data, and operations stakeholders.
The role is hybrid in Singapore, with occasional travel across Asia-Pacific 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:
Build client artificial intelligence solutions and keep them running reliably in production.
Design and engineer agentic and generative artificial intelligence systems, including agents and multi-agent workflows, retrieval-augmented generation, copilots, intelligent document processing, and applied machine-learning solutions using real client data.
Integrate artificial intelligence solutions into client enterprise systems to a client-grade production standard.
Evaluate and select frontier and open-weight models based on capability, cost, latency, context-window requirements, data residency, security, and other use-case considerations.
Make defensible, benchmark-based model-selection decisions and determine when proprietary, open-weight, sovereign, or regional models are the best fit for a client environment.
Own the deployment and serving path for artificial intelligence solutions, including containerization, orchestration, continuous integration and delivery for models and agents, model serving, and inference infrastructure.
Own the production run-state after deployment, including monitoring, observability, evaluation, drift detection, retraining, re-prompting, and ongoing operational reliability.
Define and meet service-level objectives and help establish the operational standards required to turn working prototypes into dependable client services.
Own or contribute to on-call p