AI Engineer, Virtual Insurance
AIFT Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at AIFT. Taiwan.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and launch generative AI applications, using LLMs and methods such as RAG, agents, and structured outputs
- Connect AI features to current products, internal platforms, and business processes
- Create the backend services, APIs, and data pipelines that keep AI applications running in production
- Compare models and approaches, then track quality, reliability, speed, and cost to improve results
- Work with product, engineering, data, and business teams to shape use cases and move viable prototypes into production
What you bring
- At least three years in software engineering, machine learning engineering, AI engineering, or a related area
- Strong Python skills and hands-on experience developing applications with LLMs or generative AI
- Practical knowledge of RAG, embeddings, vector databases, prompt design, and LLM application architecture
- Experience building APIs, backend services, or production applications, plus familiarity with a cloud platform
Who this fits
This role suits an engineer who can investigate unclear problems, test ideas quickly, and deliver working solutions. You’ll collaborate with both technical and business teams.
From the employer
[Job Overview]
We are looking for an experienced AI Engineer to join our engineering team and help drive the development and adoption of AI solutions across the organization. We build digital-first products and continuously explore how AI can improve our products, operations, and the way our teams work.
You will apply Generative AI and modern AI technologies to real-world business problems, working closely with engineering, product, data, and business teams to turn AI ideas into scalable, production-ready solutions.
[Responsibilities]
· Design, develop, and deploy AI-powered applications and services, with a focus on Generative AI and Large Language Models (LLMs).
· Build AI solutions using techniques such as RAG, prompt engineering, tool/function calling, agents, and structured outputs.
· Integrate LLMs and AI capabilities into existing products, internal platforms, and business workflows.
· Develop and maintain backend services, APIs, and data pipelines required to support AI applications in production.
· Evaluate and experiment with different models, frameworks, and approaches to identify the most effective solution for specific use cases.
· Build evaluation and monitoring mechanisms to continuously improve AI quality, reliability, latency, and cost.
· Work closely with Product, Engineering, Data, and business stakeholders to identify high-impact AI opportunities and translate business needs into technical solutions.
· Prototype new AI ideas quickly, validate their feasibility, and turn successful prototypes into reliable production systems.
· Stay current with developments in Generative AI and proactively explore technologies that can create meaningful business impact.
[Requirements]
· 3+ years of experience in software engineering, machine learning engineering, AI engineering, or a related field.
· Strong programming skills in Python and solid software engineering fundamentals.
· Hands-on experience building applications using LLMs or Generative AI.
· Experience working with commercial or open-source LLMs and related APIs/frameworks.
· Practical understanding of RAG, embeddings, vector databases, prompt engineering, and LLM application architecture.
· Experience designing and developing APIs, backend services, or production-grade applications.
· Familiarity with cloud platforms such as AWS, GCP, or Azure.
· Good understanding of databases, data processing, and system integration.
· Ability to independently explore ambiguous problems, experiment quickly, and turn ideas into working solutions.
· Strong communication and collaboration skills, with the ability to work effectively across technical and non-technical teams.
[Nice to Have]
· Experience building AI agents or agentic workflows, including multi-step reasoning and tool integration.
· Experience with LLM evaluation, observability, guardrails, or AI application monitoring.
· Experience with vector databases or search technologies such as Elasticsearch, OpenSearch, Pinecone, Weaviate, or similar tools.
· Experience with MLOps / LLMOps, model deployment, CI/CD, Docker, or Kubernetes.
· Experience optimizing AI applications for latency, scalability, reliability, and cost.
· Experience with machine learning, NLP, recommendation systems, or other applied AI domains.
· Experience working in fintech, insurtech, financial services, or other regulated industries.
· Experience contributing to AI adoption, automation, or developer productivity initiatives within an organization.