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.

adKey Responsibilit

• iesStrategic Roadmap: Partner with business heads (Individual Life, Group Medical, Claims, Agency Distribution) to target friction points and drive broad AI adoption across the enterpr
• iseFinancial & Project Governance: Define clear ROI frameworks for AI initiatives, navigating the "buy vs. build" paradigm, managing vendor SLAs, and controlling budget allocati
• onsChange Management: Lead cross-functional squads to ensure front-line agents, underwriters, and claims adjusters smoothly transition to AI-assisted workfl
• owsRegulatory 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 guidelin

es.Requirem

• ents10+ years of experience leading large-scale digital transformation or technology consulting projects within Financial Serv
• icesStrong baseline knowledge of what GenAI and ML can (and cannot) realistically achieve in produc
• tionExceptional executive communication skills; bilingual fluency (English and Cantonese/Mandarin) is highly preferred for regional stakeholder managem

ent.Lead AI Arch

• litiesTarget State Blueprinting: Design scalable, secure multi-cloud AI infrastructure (Azure AI, AWS Bedrock, or Alibaba Cloud) integrated deeply with legacy core systems (e.g., AS400, Life
• /400).Enterprise Guardrails: Architect robust semantic caching frameworks, context-window compression techniques, and model routing layers to maximize throughput and minimize token expend
• iture.Data & Retrieval Architecture: Blueprint highly secure, low-latency Vector Database frameworks (Pinecone, Milvus, or Qdrant) alongside hybrid-search and Agentic RAG pat
• terns.Technical Evaluation: Establish strict automated assessment frameworks (e.g., Ragas, TruLens) to continuously monitor hallucination, bias, and data leakage

risks.Requir

• ements7+ years in solution architecture, with a minimum of 2 years spent explicitly designing production-grade LLM or machine learning pipe
• lines.Deep knowledge of cloud-native infrastructure, microservice mesh, and enterprise security boundaries (e.g., private endpoints, IAM poli
• cies).Proven experience working alongside security and data governance officers in highly regulated environ

ments.Senior AI E

• bilitiesAgentic 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 b
• indings.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 micros

services.Requ

• irements5 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 optimi
• zations.Strong alignment with Agile methodologies, Git workflows, and CI/CD aut