{"id":645162,"url":"https://alion.io/job/cfgi-data-ai-artificial-intelligence-engineer","title":"Data & AI - Artificial Intelligence Engineer","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":77,"open_postings":80,"ghost_share":0.3,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":428,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":{"min_usd":59000,"max_usd":130000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"Blue-Green Deployment","optional":false},{"name":"Canary Release","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Cohere SDK","optional":false},{"name":"CrewAI","optional":false},{"name":"DeepEval","optional":false},{"name":"DeepSeek","optional":false},{"name":"Dify","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Evidently AI","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Gemma","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"Least Privilege","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Ollama","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"SGLang","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-08-19T02:33:56Z","employer_posted_date":"2026-08-19","last_verified_at":"2026-11-01T17:07:05Z","board_verified":true,"closed_at":null,"days_open":74,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":74},"description":"Data & AI - Artificial Intelligence Engineer\nCFGI 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.\nThis 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.\n“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.\nThis 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.\nThe 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.\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, and industrials. Private equity will also serve as a cross-cutting channel across sectors.\nWhat you might expect:\nBuild client artificial intelligence solutions and keep them running reliably in production.\nDesign 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.\nIntegrate artificial intelligence solutions into client enterprise systems to a client-grade production standard.\nEvaluate and select frontier and open-weight models based on capability, cost, latency, context-window requirements, data residency, security, and other use-case considerations.\nMake defensible, benchmark-based model-selection decisions and determine when proprietary, open-weight, sovereign, or regional models are the best fit for a client environment.\nOwn 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.\nOwn the production run-state after deployment, including monitoring, observability, evaluation, drift detection, retraining, re-prompting, and ongoing operational reliability.\nDefine and meet service-level objectives and help establish the operational standards required to turn working prototypes into dependable client services.\nOwn or contribute to on-call processes, runbooks, incident severity frameworks, incident response, and post-incident reviews.\nImplement safe release practices, including rollback, canary deployment, blue-green deployment, disaster recovery, backup restoration, capacity testing, and performance testing.\nMonitor and manage cost, latency, token usage, and infrastructure economics so production artificial intelligence solutions remain commercially sustainable.\nBuild automated evaluation and quality gates for models and agents covering accuracy, hallucination, robustness, and safety.\nEstablish release gates before solutions reach production and continuous evaluation processes once they are live.\nEmbed 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.\nCapture the control evidence and audit trail required by regulated clients.\nPartner closely with the data engineer on artificial-intelligence-ready data, including retrieval corpora, embeddings, feature inputs, data contracts, lineage, and governed data pipelines.\nCreate reusable engineering and deployment patterns, accelerators, templates, and repeatable paths to production.\nHelp establish the engineering, deployment, machine-learning-operations, large-language-model-operations, and agent-operations standards for the practice.\nMentor junior engineers and help raise the technical standard of the team as the practice scales.\nWork 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.\nWhat you must have:\nApproximately 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.\nHands-on experience taking artificial intelligence or machine-learning solutions through the full production lifecycle, including build, deployment, monitoring, evaluation, and ongoing production operation.\nExperience delivering technical solutions in a client-facing, business-facing, or executive-stakeholder environment and explaining complex artificial intelligence solutions clearly to non-technical stakeholders.\nWhat sets you apart:\nExpert Python skills and substantial hands-on experience developing production artificial intelligence and machine-learning solutions.\nDirect experience working with leading frontier-model application programming interfaces and evaluating proprietary and open-weight model families.\nStrong understanding of model selection and benchmark-based evaluation across capability, cost, latency, context window, security, language, and data-residency requirements.\nExperience evaluating frontier proprietary models such as Anthropic Claude, OpenAI GPT, Google Gemini, or comparable technologies.\nFamiliarity 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.\nAwareness 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.\nExperience making model-selection decisions where data residency, language, sovereignty, licensing, or regulatory requirements influence the architecture.\nProduction 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.\nExperience designing agents and multi-agent workflows using tool use, function calling, and Model Context Protocol integrations to connect models with enterprise data and systems.\nProduction experience with retrieval-augmented generation, including embedding models, chunking strategies, retrieval design, hybrid search, and vector databases.\nExperience with vector technologies such as Pinecone, Weaviate, Qdrant, pgvector, or comparable platforms.\nExperience with prompt engineering, prompt management, versioning, and production governance.\nStrong machine-learning-operations, large-language-model-operations, and/or agent-operations experience supporting production artificial intelligence systems.\nExperience with containerization and orchestration technologies such as Docker and Kubernetes.\nExperience establishing continuous integration and delivery processes for artificial intelligence systems, models, and agents using technologies such as GitHub Actions or comparable tooling.\nExperience with model serving and inference infrastructure such as vLLM, SGLang, or comparable technologies.\nHands-on experience with managed artificial intelligence platforms such as Amazon Bedrock, Google Vertex AI, Microsoft Foundry / Azure OpenAI, or comparable environments.\nUnderstanding of self-hosted inference approaches for regulated or data-residency-constrained environments, including technologies such as Ollama or comparable deployment models.\nExperience with experiment tracking, model registries, generative artificial intelligence tracing, and model lifecycle management using technologies such as MLflow or comparable tooling.\nExperience with infrastructure-as-code technologies such as Terraform or comparable platforms.\nExperience establishing model and agent evaluation frameworks for production artificial intelligence systems.\nFamiliarity with evaluation technologies such as Ragas, DeepEval, or comparable frameworks.\nExperience with artificial intelligence observability and output monitoring platforms such as LangSmith, Arize Phoenix, Evidently AI, or comparable technologies.\nExperience with drift detection, data lineage, data contracts, and production monitoring for audit-ready systems.\nHands-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.\nUnderstanding of the architectural trade-offs between managed artificial intelligence services and self-hosted inference in regulated or data-residency-sensitive environments.\nPractical experience securing production agentic and generative artificial intelligence systems.\nUnderstanding of prompt injection, tool-authorization controls, least-privilege identity, sandboxing, human approval gates, memory integrity, auditability, and data-leak-prevention controls.\nExperience with software-supply-chain security practices, including secrets management, key rotation, dependency and vulnerability scanning, software bills of materials, and related supply-chain controls.\nDemonstrated production accountability for artificial intelligence or machine-learning services, including monitoring, incident management, reliability, performance, and operational support.\nExperience defining or working against service-level objectives, production runbooks, release controls, rollback processes, and incident-management procedures.\nExperience with safe production-release practices such as canary or blue-green deployment.\nExperience with disaster recovery, backup restoration, capacity management, performance testing, and production resilience.\nExperience monitoring and optimizing artificial intelligence infrastructure costs, inference latency, and token consumption.\nA practical understanding of artificial intelligence governance, model risk, privacy, and data-residency requirements in regulated environments.\nWorking 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.\nFamiliarity with artificial intelligence governance and privacy developments across major Asia-Pacific markets, including Japan, South Korea, China, India, Australia, and Southeast Asia.\nExperience designing or operating artificial intelligence solutions where jurisdiction-specific privacy, data-transfer, localization, security, or regulatory requirements materially influence technical implementation.\nNice to have:\nHealthcare and life-sciences experience involving clinical, regulatory, real-world-evidence, healthcare-data, or related artificial intelligence use cases.\nExperience 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 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