Data & AI - Artificial Intelligence Engineer

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 processes, runbooks, incident severity frameworks, incident response, and post-incident reviews.

Implement safe release practices, including rollback, canary deployment, blue-green deployment, disaster recovery, backup restoration, capacity testing, and performance testing.

Monitor and manage cost, latency, token usage, and infrastructure economics so production artificial intelligence solutions remain commercially sustainable.

Build automated evaluation and quality gates for models and agents covering accuracy, hallucination, robustness, and safety.

Establish release gates before solutions reach production and continuous evaluation processes once they are live.

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

Capture the control evidence and audit trail required by regulated clients.

Partner closely with the data engineer on artificial-intelligence-ready data, including retrieval corpora, embeddings, feature inputs, data contracts, lineage, and governed data pipelines.

Create reusable engineering and deployment patterns, accelerators, templates, and repeatable paths to production.

Help establish the engineering, deployment, machine-learning-operations, large-language-model-operations, and agent-operations standards for the practice.

Mentor junior engineers and help raise the technical standard of the team as the practice scales.

Work directly with the Partner, Data & AI Innovation and the wider Data & AI team to shape how CFGI delivers and operates production artificial intelligence solutions across Asia-Pacific.

What you must have:

Approximately 5–10 years of experience in artificial intelligence engineering, machine-learning engineering, software engineering, data engineering, machine-learning operations, and/or related technical delivery, including substantial hands-on experience building and shipping production artificial intelligence or machine-learning systems.

Hands-on experience taking artificial intelligence or machine-learning solutions through the full production lifecycle, including build, deployment, monitoring, evaluation, and ongoing production operation.

Experience delivering technical solutions in a client-facing, business-facing, or executive-stakeholder environment and explaining complex artificial intelligence solutions clearly to non-technical stakeholders.

What sets you apart:

Expert Python skills and substantial hands-on experience developing production artificial intelligence and machine-learning solutions.

Direct experience working with leading frontier-model application programming interfaces and evaluating proprietary and open-weight model families.

Strong understanding of model selection and benchmark-based evaluation across capability, cost, latency, context window, security, language, and data-residency requirements.

Experience evaluating frontier proprietary models such as Anthropic Claude, OpenAI GPT, Google Gemini, or comparable technologies.

Familiarity with open-weight and self-deployable model families such as Meta Llama, Google Gemma, Microsoft Phi, Mistral, Cohere Command, Alibaba Qwen, DeepSeek, Zhipu AI GLM, Moonshot AI Kimi, 01.AI Yi, or comparable technologies.

Awareness of regional and sovereign artificial intelligence models relevant to Asia-Pacific deployments, including Baidu ERNIE, ByteDance Doubao, Naver HyperCLOVA X, LG EXAONE, ELYZA, AI Singapore SEA-LION, SCB 10X Typhoon, or comparable regional models.

Experience making model-selection decisions where data residency, language, sovereignty, licensing, or regulatory requirements influence the architecture.

Production experience building agentic and generative artificial intelligence systems using orchestration frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, Microsoft Agent Framework, Dify, AgentScope, or comparable technologies.

Experience designing agents and multi-agent workflows using tool use, function calling, and Model Context Protocol integrations to connect models with enterprise data and systems.

Production experience with retrieval-augmented generation, including embedding models, chunking strategies, retrieval design, hybrid search, and vector databases.

Experience with vector technologies such as Pinecone, Weaviate, Qdrant, pgvector, or comparable platforms.

Experience with prompt engineering, prompt management, versioning, and production governance.

Strong machine-learning-operations, large-language-model-operations, and/or agent-operations experience supporting production artificial intelligence systems.

Experience with containerization and orchestration technologies such as Docker and Kubernetes.

Experience establishing continuous integration and delivery process