Data & AI - Artificial Intelligence Engineer

Job description

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 processes for artificial intelligence systems, models, and agents using technologies such as GitHub Actions or comparable tooling. Experience with model serving and inference infrastructure such as vLLM, SGLang, or comparable technologies. Hands-on experience with managed artificial intelligence platforms such as Amazon Bedrock, Google Vertex AI, Microsoft Foundry / Azure OpenAI, or comparable environments. Understanding of self-hosted inference approaches for regulated or data-residency-constrained environments, including technologies such as Ollama or comparable deployment models. Experience with experiment tracking, model registries, generative artificial intelligence tracing, and model lifecycle management using technologies such as MLflow or comparable tooling. Experience with infrastructure-as-code technologies such as Terraform or comparable platforms. Experience establishing model and agent evaluation frameworks for production artificial intelligence systems. Familiarity with evaluation technologies such as Ragas, DeepEval, or comparable frameworks. Experience with artificial intelligence observability and output monitoring platforms such as LangSmith, Arize Phoenix, Evidently AI, or comparable technologies. Experience with drift detection, data lineage, data contracts, and production monitoring for audit-ready systems. Hands-on cloud delivery experience with at least one major cloud platform such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform and its artificial intelligence and machine-learning services. Understanding of the architectural trade-offs between managed artificial intelligence services and self-hosted inference in regulated or data-residency-sensitive environments. Practical experience securing production agentic and generative artificial intelligence systems. Understanding of prompt injection, tool-authorization controls, least-privilege identity, sandboxing, human approval gates, memory integrity, auditability, and data-leak-prevention controls. Experience with software-supply-chain security practices, including secrets management, key rotation, dependency and vulnerability scanning, software bills of materials, and related supply-chain controls. Demonstrated production accountability for artificial intelligence or machine-learning services, including monitoring, incident management, reliability, performance, and operational support. Experience defining or working against service-level objectives, production runbooks, release controls, rollback processes, and incident-management procedures. Experience with safe production-release practices such as canary or blue-green deployment. Experience with disaster recovery, backup restoration, capacity management, performance testing, and production resilience. Experience monitoring and optimizing artificial intelligence infrastructure costs, inference latency, and token consumption. A practical understanding of artificial intelligence governance, model risk, privacy, and data-residency requirements in regulated environments. Working awareness of Singapore’s artificial intelligence and privacy environment, including the Personal Data Protection Act, AI Verify, and Singapore’s Model Artificial Intelligence Governance Framework for Agentic Artificial Intelligence. Familiarity with artificial intelligence governance and privacy developments across major Asia-Pacific markets, including Japan, South Korea, China, India, Australia, and Southeast Asia. Experience designing or operating artificial intelligence solutions where jurisdiction-specific privacy, data-transfer, localization, security, or regulatory requirements materially influence technical implementation. 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. Who thrives here: A hands-on engineer who enjoys both building artificial intelligence solutions and owning what happens after they reach production. Someone who sees deployment, monitoring, reliability, controls, and operational performance as part of engineering rather than a handoff to another team. Strong engineering judgment with the ability to distinguish a compelling prototype from a production-ready client service. Comfortable working across artificial intelligence engineering, machine-learning operations, cloud infrastructure, data, security, and production operations. Strong analytical and problem-solving abilities with an ability to diagnose and resolve complex technical and production issues. Excellent interpersonal, written, and verbal communication skills with the ability to work effectively with engineers, data professionals, client stakeholders, and executive buyers. Able to balance model quality and innovation with reliability, security, governance, cost, latency, and operational requirements. Entrepreneurial, self-motivated, adaptable, ethical, and dependable. Comfortable operating in a small, fast-scaling team where roles are broad and individual contributors materially shape engineering standards. High energy with a commitment to technical quality, client service, team development, and practice growth. Take ownership of your career at CFGI: Join the founding Data & AI build team in Singapore and help define how the practice builds, deploys, and operates artificial intelligence solutions. Work across the full artificial intelligence lifecycle — from first build through governed production operation. Take genuine ownership of both the solution and its production run-state rather than handing deployment and operations to another team. Help establish reusable engineering, deployment, evaluation, observability, and machine-learning-operations standards for a growing practice. Work within a global firm with an established Data & Analytics practice, controls capabilities, and an artificial-intelligence-native managed-services platform. Take a vendor-agnostic approach to client problems and select models and technologies based on what best meets client requirements. Work across sectors while developing particular depth in healthcare and life sciences and other regulated environments. Build directly alongside data engineers, data scientists, artificial intelligence architects, and CFGI’s wider APAC and global teams. Influence the technical standards of the practice from an early stage and help create the repeatable path from prototype to dependable production service. Mentor junior engineers and develop toward broader lead-engineering and senior technical-leadership opportunities as CFGI’s Asia-Pacific Data & AI practice scales. CFGI is an equal-opportunity employer and selects candidates based on job-related capability without regard to nationality, ethnicity, race, religion, gender, age, or any other protected characteristic. All qualified applicants are encouraged to apply. Work-authorization sponsorship may be supported but remains subject to Singapore Ministry of Manpower approval and applicable work-pass eligibility. Where required in connection with an Employment Pass application, the role will be advertised in accordance with applicable Singapore requirements. 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 processes for artificial intelligence systems, models, and agents using technologies such as GitHub Actions or comparable tooling. Experience with model serving and inference infrastructure such as vLLM, SGLang, or comparable technologies. Hands-on experience with managed artificial intelligence platforms such as Amazon Bedrock, Google Vertex AI, Microsoft Foundry / Azure OpenAI, or comparable environments. Understanding of self-hosted inference approaches for regulated or data-residency-constrained environments, including technologies such as Ollama or comparable deployment models. Experience with experiment tracking, model registries, generative artificial intelligence tracing, and model lifecycle management using technologies such as MLflow or comparable tooling. Experience with infrastructure-as-code technologies such as Terraform or comparable platforms. Experience establishing model and agent evaluation frameworks for production artificial intelligence systems. Familiarity with evaluation technologies such as Ragas, DeepEval, or comparable frameworks. Experience with artificial intelligence observability and output monitoring platforms such as LangSmith, Arize Phoenix, Evidently AI, or comparable technologies. Experience with drift detection, data lineage, data contracts, and production monitoring for audit-ready systems. Hands-on cloud delivery experience with at least one major cloud platform such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform and its artificial intelligence and machine-learning services. Understanding of the architectural trade-offs between managed artificial intelligence services and self-hosted inference in regulated or data-residency-sensitive environments. Practical experience securing production agentic and generative artificial intelligence systems. Understanding of prompt injection, tool-authorization controls, least-privilege identity, sandboxing, human approval gates, memory integrity, auditability, and data-leak-prevention controls. Experience with software-supply-chain security practices, including secrets management, key rotation, dependency and vulnerability scanning, software bills of materials, and related supply-chain controls. Demonstrated production accountability for artificial intelligence or machine-learning services, including monitoring, incident management, reliability, performance, and operational support. Experience defining or working against service-level objectives, production runbooks, release controls, rollback processes, and incident-management procedures. Experience with safe production-release practices such as canary or blue-green deployment. Experience with disaster recovery, backup restoration, capacity management, performance testing, and production resilience. Experience monitoring and optimizing artificial intelligence infrastructure costs, inference latency, and token consumption. A practical understanding of artificial intelligence governance, model risk, privacy, and data-residency requirements in regulated environments. Working awareness of Singapore’s artificial intelligence and privacy environment, including the Personal Data Protection Act, AI Verify, and Singapore’s Model Artificial Intelligence Governance Framework for Agentic Artificial Intelligence. Familiarity with artificial intelligence governance and privacy developments across major Asia-Pacific markets, including Japan, South Korea, China, India, Australia, and Southeast Asia. Experience designing or operating artificial intelligence solutions where jurisdiction-specific privacy, data-transfer, localization, security, or regulatory requirements materially influence technical implementation. 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. Who thrives here: A hands-on engineer who enjoys both building artificial intelligence solutions and owning what happens after they reach production. Someone who sees deployment, monitoring, reliability, controls, and operational performance as part of engineering rather than a handoff to another team. Strong engineering judgment with the ability to distinguish a compelling prototype from a production-ready client service. Comfortable working across artificial intelligence engineering, machine-learning operations, cloud infrastructure, data, security, and production operations. Strong analytical and problem-solving abilities with an ability to diagnose and resolve complex technical and production issues. Excellent interpersonal, written, and verbal communication skills with the ability to work effectively with engineers, data professionals, client stakeholders, and executive buyers. Able to balance model quality and innovation with reliability, security, governance, cost, latency, and operational requirements. Entrepreneurial, self-motivated, adaptable, ethical, and dependable. Comfortable operating in a small, fast-scaling team where roles are broad and individual contributors materially shape engineering standards. High energy with a commitment to technical quality, client service, team development, and practice growth. Take ownership of your career at CFGI: Join the founding Data & AI build team in Singapore and help define how the practice builds, deploys, and operates artificial intelligence solutions. Work across the full artificial intelligence lifecycle — from first build through governed production operation. Take genuine ownership of both the solution and its production run-state rather than handing deployment and operations to another team. Help establish reusable engineering, deployment, evaluation, observability, and machine-learning-operations standards for a growing practice. Work within a global firm with an established Data & Analytics practice, controls capabilities, and an artificial-intelligence-native managed-services platform. Take a vendor-agnostic approach to client problems and select models and technologies based on what best meets client requirements. Work across sectors while developing particular depth in healthcare and life sciences and other regulated environments. Build directly alongside data engineers, data scientists, artificial intelligence architects, and CFGI’s wider APAC and global teams. Influence the technical standards of the practice from an early stage and help create the repeatable path from prototype to dependable production service. Mentor junior engineers and develop toward broader lead-engineering and senior technical-leadership opportunities as CFGI’s Asia-Pacific Data & AI practice scales. CFGI is an equal-opportunity employer and selects candidates based on job-related capability without regard to nationality, ethnicity, race, religion, gender, age, or any other protected characteristic. All qualified applicants are encouraged to apply. Work-authorization sponsorship may be supported but remains subject to Singapore Ministry of Manpower approval and applicable work-pass eligibility. Where required in connection with an Employment Pass application, the role will be advertised in accordance with applicable Singapore requirements.