AI Engineer - Remote

• 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.

• 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.

Nice to have:

• Healthcare and life-sciences experience involving clinical, regulatory, real-world-evidence, healthcare-data, or related artificial intelligence use cases.

• Experience with healthcare data standards, interoperability frameworks, and clinical ontologies such as Fast Healthcare Interoperability Resources (FHIR), Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), or comparable standards.

• Office-of-the-Chief-Financial-Officer experience involving artificial intelligence for financial close, financial reporting, controls, risk, finance operations, or related use cases.

• A recognized cloud, artificial intelligence, machine-learning, machine-learning-operations, security, or related technical certification.

• Experience establishing a repeatable path to production for a growing artificial intelligence, engineering, or data team.

• Experience creating reusable engineering standards, deployment templates, accelerators, or production operating models within a consulting or client-delivery environment.

• Receive 100% salary from the onboarding date.

• Participate in company activities: Teambuilding, travel, vacation and other activities.

• Work with large and advanced systems, have the opportunity to develop comprehensive technology skills with complex problems, requiring high accuracy.