{"id":645179,"url":"https://alion.io/job/cfgi-data-ai-artificial-intelligence-architect","title":"Data & AI - Artificial Intelligence Architect","company":{"id":672933,"name":"CFGI","domain":"cfgi.com","url":"https://alion.io/company/cfgi","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":82,"ghost_share":0.22,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":555,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"Cohere SDK","optional":false},{"name":"CrewAI","optional":false},{"name":"DeepSeek","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Gemma","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangGraph","optional":false},{"name":"Least Privilege","optional":false},{"name":"Llama","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Vertex AI","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-08-19T02:32:54Z","employer_posted_date":"2026-08-19","last_verified_at":"2026-10-28T20:12:14Z","board_verified":true,"closed_at":null,"days_open":70,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":70},"description":"Data & AI - Artificial Intelligence Architect\nCFGI is building its Data & AI practice in Singapore and is seeking a hands-on Artificial Intelligence Architect to own the solution architecture of client artificial intelligence builds and lead the team’s technical design.\nAs the team’s lead architect and client-facing technical lead, you will design reference architectures - agent-first where open-ended reasoning or tool selection genuinely adds value, and conventional or deterministic designs where those are the better answer - set integration, security, and controls standards, own model and vendor strategy, and guide the engineering team in building solutions that are scalable, governed, and audit-ready.\nThis is a Manager-level role based in Singapore, working with the Data & AI build team, including artificial intelligence engineers, a data engineer, and data scientists; CFGI’s APAC and global Data & AI, controls, and managed-services practices; and client Chief Technology Officers, Chief Data Officers, enterprise architects, and security and risk leaders.\nThe role is hybrid in Singapore, with travel across Asia-Pacific expected for client work. Work-authorization sponsorship may be supported where required, subject to applicable eligibility and approval requirements.\nHealthcare 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,\nand industrials. Private equity will also serve as a cross-cutting channel across sectors.\nWhat you might expect:\nOwn the solution architecture of the practice’s client artificial intelligence work and lead the team’s technical design from strategy and use-case definition through production deployment.\nTranslate client strategies and use cases into build-ready reference architectures covering components, integration patterns, data flows, model choices, security controls, governance requirements, and delivery approaches.\nDetermine when agentic architecture genuinely adds value over conventional or deterministic solutions and establish appropriate design patterns for each.\nDesign reusable agent-first and enterprise artificial intelligence reference architectures, including multi-agent orchestration, agent-to-agent and Model Context Protocol tool layers, model gateways, retrieval and knowledge architectures, and integration patterns connecting artificial intelligence solutions to the client’s broader enterprise estate.\nOwn model and vendor strategy and third-party model risk, including the approach to model selection across frontier and open-weight providers.\nEvaluate platforms, models, and tooling based on capability, cost, latency, context requirements, data residency, security, portability, and business needs.\nDesign for model and vendor provenance, contractual controls, portability, and exit so that clients can make defensible technology decisions without unnecessary vendor lock-in.\nEmbed security and controls into architecture from the outset, including prompt-injection defenses, least-privilege agent identity, tool authorization, sandboxing, human approval gates, data-leak prevention, memory integrity, data sovereignty, auditability, and evidence requirements.\nDesign architectures capable of satisfying the governance, control, and audit expectations of regulated clients, boards, security teams, and regulators.\nSet and maintain the technical standard for the practice, including architecture principles, design patterns, non-functional requirements, technical assurance, and design-review processes across engagements.\nReview and assure engineering work while guiding artificial intelligence engineers, the data engineer, and data scientists toward consistent technical and architectural standards.\nMake critical build-versus-buy, platform, model-selection, integration, data, and architecture decisions across client engagements.\nServe as the client-facing technical lead and run architecture and design sessions with Chief Technology Officers, Chief Data Officers, enterprise architects, security leaders, risk leaders, and other technical and executive stakeholders.\nTranslate complex business problems into governed technical solutions and communicate architecture credibly to both technical teams and executive buyers.\nContribute technical solutioning to proposals and pursuits by shaping the technical approach, delivery plan, architecture, and risk position alongside practice leadership.\nBuild reusable practice assets, including reference architectures, architecture patterns, technical standards, methodologies, and accelerators.\nMentor the engineering team and raise the technical bar as the Data & AI practice grows.\nWork with the Partner, Data & AI Innovation and, as the practice grows, the wider CFGI architecture community on enterprise-level technical and architecture decisions.\nWhat you must have:\nApproximately 6-10 years of experience in technical delivery, solution architecture, enterprise architecture, data architecture, cloud architecture, artificial intelligence architecture, and/or related technology consulting, including substantial experience architecting production artificial intelligence, data, or platform systems and leading technical work.\nHands-on experience architecting production generative artificial intelligence and/or agentic systems, including orchestration, retrieval or knowledge architectures, model integration, enterprise integration patterns, and the transition of artificial intelligence solutions from design into production.\nExperience serving as a client-facing technical lead, translating business requirements into technical architecture and working directly with enterprise architects, technology leaders, security or risk stakeholders, and executive decision-makers.\nWhat sets you apart:\nDeep experience designing agentic and generative artificial intelligence systems, including multi-agent orchestration, agent-to-agent patterns, Model Context Protocol, model gateways, retrieval-augmented generation, vector architectures, knowledge graphs, prompt strategy, and system-level evaluation.\nExperience with agentic frameworks such as LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, or comparable orchestration technologies.\nStrong understanding of frontier and open-weight model ecosystems and experience developing model-selection strategies based on capability, cost, latency, context window, data residency, security, and operational requirements.\nExperience evaluating proprietary model families such as Anthropic Claude, OpenAI GPT, and Google Gemini as well as open-weight models such as Meta Llama, Google Gemma, Microsoft Phi, Mistral, Cohere Command, or comparable technologies.\nFamiliarity with models and artificial intelligence ecosystems relevant to Asia-Pacific markets, including Alibaba Qwen, DeepSeek, Zhipu AI GLM, Moonshot AI Kimi, 01.AI Yi, Naver HyperCLOVA X, LG EXAONE, ELYZA, AI Singapore SEA-LION, or similar regional and sovereign models.\nDeep architecture experience with one or more major cloud environments such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform and their managed artificial intelligence and machine-learning services.\nExperience with Microsoft Foundry and Azure OpenAI, Amazon Bedrock, Google Vertex AI, or comparable managed artificial intelligence platforms.\nUnderstanding of sovereign and regional cloud environments relevant to Asia-Pacific data-residency requirements, including Alibaba Cloud, Tencent Cloud, Huawei Cloud, Naver Cloud, locally operated global-cloud environments, and/or self-hosted inference architectures.\nExperience designing integration architecture across application programming interfaces, event-streaming technologies, identity platforms, enterprise applications, and data environments.\nWorking knowledge of modern data and machine-learning-operations architecture, including lakehouse architectures, semantic layers, observability, deployment, monitoring, and production operations.\nExperience designing security architecture for production artificial intelligence and agentic systems, including prompt-injection defense, tool authorization, least-privilege identity, sandboxing, human approval gates, memory integrity, auditability, data privacy, and data sovereignty.\nExperience incorporating artificial intelligence governance, model risk, control mapping, evidence, and audit requirements into enterprise architecture.\nFamiliarity with major artificial intelligence governance frameworks and standards, including the European Union Artificial Intelligence Act, the United States National Institute of Standards and Technology Artificial Intelligence Risk Management Framework and Generative Artificial Intelligence Profile, ISO/IEC 42001, Singapore AI Verify, and Singapore’s Model Artificial Intelligence Governance Framework for Agentic Artificial Intelligence.\nFamiliarity with emerging Asia-Pacific artificial intelligence regulatory and governance regimes, including relevant frameworks in Japan, South Korea, China, India, Australia, Singapore, and other Southeast Asian markets.\nPractical experience addressing data privacy, residency, and sovereignty requirements across multiple jurisdictions.\nFamiliarity with privacy and data-protection regimes such as Singapore’s Personal Data Protection Act, Japan’s Act on the Protection of Personal Information, South Korea’s Personal Information Protection Act, India’s Digital Personal Data Protection Act, China’s Personal Information Protection Law and Data Security Law, Australia’s Privacy Act, and related Southeast Asian requirements.\nExperience designing controls and architecture jurisdiction by jurisdiction rather than retrofitting residency, sovereignty, privacy, and regulatory requirements after implementation.\nDemonstrated ability to make build-versus-buy decisions, evaluate third-party artificial intelligence vendors and models, assess model risk, and design for portability and vendor exit.\nExperience leading architecture and technical-design sessions and communicating complex technical decisions credibly to Chief Technology Officers, enterprise architects, security leaders, risk leaders, executive buyers, and other senior stakeholders.\nExperience contributing technical solutioning, architecture, delivery planning, and risk considerations to proposals, pursuits, and client presentations.\nExperience establishing architecture principles, reusable patterns, non-functional requirements, design-review processes, technical-assurance standards, or other architecture-governance mechanisms.\nExperience mentoring engineers, data professionals, or other technical team members and providing technical review and architecture guidance.\nNice to have:\nHealthcare and life-sciences architecture experience involving clinical, regulatory, real-world-evidence, healthcare-data, or related systems.\nExperience with healthcare interoperability standards, clinical ontologies, and data models 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 healthcare standards.\nExperience designing artificial intelligence and data solutions within healthcare, life sciences, or another highly regulated environment.\nOffice-of-the-Chief-Financial-Officer architecture experience involving artificial intelligence, data, financial close, reporting, controls, risk, or other finance-related use cases.\nTOGAF Enterprise Architecture Practitioner, ISO/IEC 42001 lead-implementer, or another relevant architecture, cloud, security, or artificial intelligence-governance credential.\nPublished thought leadership related to artificial intelligence, architecture, data, security, or governance.\nExperience establishing or scaling an architecture capability within a growing consulting practice, technology 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