AI Product Builder

The AI Product Builder isa senior, hands-on role within the Data Office of Allianz Insurance Singapore. You will design, build, deploy, and scale AI-powered solutions that deliver measurable business outcomes across multiple functions, while also improving personal productivity.You will own the full solution lifecycle: from scoping business problems and selecting the right tools (such as Azure AI Foundry, Copilot Studio, Claude, AllianzGPT, or external solutions), to building end-to-end solutions that include data pipelines, integrations, and deployment, and then driving adoption and iterating based on real usage. The role is approximately 70% hands-on delivery and 30% enablement and stakeholder engagement.You will be the primary AI builder in a lean Data Office, reporting to the Head of Data & AI. You will collaborate with Allianz Technology (AZT) on infrastructure and security and work directly with business stakeholders and senior leadership.This role offers direct exposure to senior leadership, the freedom to work across multiple platforms, and the opportunity to create meaningful impact in insurance and AI.Key Responsibilities:Build AI Solutions for Business ProblemsYou will take business problems from scoping through to production.Deliver end-to-end AI solutions (agents, copilots, RAG systems, automation, ML models) for priority use cases — including all necessary data pipelines, integrations, and deployment.Select the right tool for each problem, such as Azure AI Foundry for complex orchestration, Copilot Studio for rapid business-facing bots, Claude for reasoning-intensive tasks, or AllianzGPT where group policy requires it, with clear rationale and consideration of cost and governance trade-offs.Evaluate build-vs-buy for specialist capabilities (document AI, OCR, vendor tools). Benchmark models across providers. Make decisions that optimise for production value.Operate & Scale What's BuiltYou own reliability, cost, and reusability of everything you deployProductionise AI applications with enterprise-grade reliability - monitoring, alerting, error handling, version controlBuild modular, reusable components (document extraction, Q&A templates, data quality checks) that accelerate future delivery. Maintain a component library that compounds your impact over timeOwn FinOps for AI workloads by tracking consumption, reporting cost per solution, and optimising proactively through model routing, caching, and right-sizingEnable Others to Use & Extend AISuccess in this role is measured not only by what you build, but also by how effectively you enable others to use and extend AI independently.Deliver targeted training to business teams, tailored to their level of AI literacy and focused on practical adoption as well as awareness. You will help teams build confidence by demonstrating value through working solutions.Create enablement assets that scale beyond your direct involvement: prompt libraries, tool-selection decision guides, self-service playbooks, recorded walkthroughsProduce executive-quality impact narratives and demos for senior leadership. Translate technical delivery into business language that sustains investment and sponsorship.Govern & Navigate the EnterpriseYou will deliver within Allianz's governance, regulatory, and organisational environment.Ensure all deployed solutions comply with Allianz Group AI governance, MAS principles, and data classification requirements, including PDPA. Register solutions in the RAI tool and ensure the appropriate assessments are completed.Engage business stakeholders (Claims, Underwriting, Distribution, Operations, Finance) to identify and scope AI use cases — managing expectations on feasibility, timelines, and governance requirements.Coordinate with Allianz Technology when relevant for provisioning, security reviews, and platform support.Skills & Experience:GenAI & LLMs: Experience building RAG systems, agents, or document intelligence solutions in production. Strong prompt engineering and LLM evaluation skills. Experience taking solutions beyond proof of concept into adoption and iteration is highly valued.AI Platforms: Hands-on Azure AI / Azure OpenAI (or equivalent cloud AI). Working knowledge of Copilot Studio or low-code AI tooling. Comfort navigating multi-model environments and making informed selection decisions.Data & Software Engineering: Python/PySpark pipelines, cloud data lakes (ADLS or equivalent), RESTAPIs, CI/CD. Production-quality code with testing and error handling — not notebook-only.Communication & Influence: Translates AI into business value for non-technical audiences. Deliver straining to mixed-literacy groups. Self-directed — structures ambiguity into action, escalates early when blocked.Cost & Vendor Awareness: Tracks AI consumption, manages budgets, evaluates vendors. Understands that every solution has a run-cost and every tool choice has a total cost of ownership.QualificationsTypically, 4-7 years of experience in AI/ML engineering or solution development, including 12-18+ months building GenAI or LLM applications in production.Degree in Computer Science, Data Science, Engineering, or related quantitative field.Experience in a regulated industry such as financial services or insurance is a plus, or you can demonstrate the ability to learn complex domains quickly.Azure certifications (AI Engineer Associate, Data Engineer Associate) or equivalent demonstrated expertise.Track record of building AI products end-to-end - not just models, but adopted solutions with user feedback loops and measurable business impact