Data & AI - Artificial Intelligence Engineer

EducationPals; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

CFGI is establishing 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.

The role focuses on delivering AI-powered use cases across healthcare, financial services, public sector, and critical infrastructure, with governance and latency considerations. You will collaborate with cross-functional teams to deploy robust AI solutions at scale.

Qualifications

• Hands-on AI engineer experience is required.
• Experience building and operating AI solutions in production.

Responsibilities

• Build client AI solutions and operate them in production.

Skills

Artificial Intelligence Engineer

Job description

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: What you must have: What sets you apart: Nice to have: Who thrives here: Take ownership of your career at CFGI: CFGI is an equal-opportunity employer and selects candidates based on job-rel

• 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.

Courses relevant to this posting

Relevance is based on skills and concepts — not a guarantee that you qualify.

Covered skills: AI fundamentals Data science LLM evaluation

Skills to close: None listed

Build the skills for this job

Coming-soon courses matched to this posting — outline, waitlist, and skill path.

1. The Five Dimensions of Data Sovereignty

Understand how location, legal entity, access paths, transfer mechanisms, and technical controls combine to determine jurisdictional exposure.

2. Evaluating Cloud and SaaS Vendor Claims

Assess vendor documentation, architecture diagrams, and contract terms to verify sovereignty promises.

3. Sovereignty in AI and Data Pipelines

Analyze sovereignty risk across embeddings, vector databases, model inference, and retrieval-augmented generation workflows.

4. Designing Compliant Multi-Region Architectures

Build reference architectures that satisfy residency, latency, resilience, and sovereignty requirements simultaneously.

5. Procurement Requirements and Contract Negotiation

Translate sovereignty needs into RFP criteria, contract clauses, and service level commitments.

Apply sovereignty frameworks to healthcare, financial services, public sector, and critical infrastructure use cases.

7. Operationalizing and Auditing Sovereignty Commitments

Implement ongoing monitoring, vendor audits, and governance processes to maintain sovereignty posture over time.

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