Insurance AI Architect / AI Engineer / AI Transformation Lead

Major insurers are scaling up production ecosystems utilizing Agentic AI, Multi-Agent workflow orchestration, and Layout-Aware Document Intelligence, and looking for talents in Enterprise Transformation Teams and newly established AI Centers of Excellence.AI Transformation LeadKey ResponsibilitiesStrategic Roadmap: Partner with business heads (Individual Life, Group Medical, Claims, Agency Distribution) to target friction points and drive broad AI adoption across the enterpriseFinancial & Project Governance: Define clear ROI frameworks for AI initiatives, navigating the "buy vs. build" paradigm, managing vendor SLAs, and controlling budget allocationsChange Management: Lead cross-functional squads to ensure front-line agents, underwriters, and claims adjusters smoothly transition to AI-assisted workflowsRegulatory Liaison: Act as the primary bridge between technical teams and Risk, Legal, and Compliance to ensure all pipelines honor Personal Data (Privacy) Ordinance (PDPO) and IA guidelines.Requirements10+ years of experience leading large-scale digital transformation or technology consulting projects within Financial ServicesStrong baseline knowledge of what GenAI and ML can (and cannot) realistically achieve in productionExceptional executive communication skills; bilingual fluency (English and Cantonese/Mandarin) is highly preferred for regional stakeholder management.Lead AI ArchitectKey ResponsibilitiesTarget State Blueprinting: Design scalable, secure multi-cloud AI infrastructure (Azure AI, AWS Bedrock, or Alibaba Cloud) integrated deeply with legacy core systems .Enterprise Guardrails: Architect robust semantic caching frameworks, context-window compression techniques, and model routing layers to maximize throughput and minimize token expenditure.Data & Retrieval Architecture: Blueprint highly secure, low-latency Vector Database frameworks (Pinecone, Milvus, or Qdrant) alongside hybrid-search and Agentic RAG patterns.Technical Evaluation: Establish strict automated assessment frameworks (e.g., Ragas, TruLens) to continuously monitor hallucination, bias, and data leakagerisks.Requirements7+ years in solution architecture, with a minimum of 2 years spent explicitly designing production-grade LLM or machine learning pipelines.Deep knowledge of cloud-native infrastructure, microservice mesh, and enterprise security boundaries (e.g., private endpoints, IAM policies).Proven experience working alongside security and data governance officers in highly regulated environments.Senior AI EngineerKey ResponsibilitiesAgentic Framework Engineering: Write clean, production-ready Python and SQL to develop complex multi-agent workflows utilizing tools like LangGraph, LangChain, or CrewAI.Intelligent Document Processing (IDP): Build state-of-the-art layout-aware document extraction pipelines using advanced OCR models to instantly ingest medical receipts and policy bindings.MLOps & Serving: Containerize applications using Docker and Kubernetes, deploying low-latency model inference endpoints via vLLM or Triton Inference Server.Rapid Prototyping: Turn abstract business requirements into high-fidelity functional Proofs-of-Concept (PoCs) in 2-week sprint cycles, then scale them seamlessly into microservices.Requirements5 years of strong backend software engineering experience with expert-level proficiency in Python.Hands-on portfolio demonstrating deployment of LLM-based systems, fine-tuning open-weight models (e.g., Llama, Qwen), or advanced vector-search optimizations.Strong alignment with Agile methodologies, Git workflows, and CI/CD automation.