AI Engineer - Insurance & Financial Services

About the role

Capco, a global technology and management consultancy focused on digital transformation in financial services and insurance, is hiring an AI Engineer for its Singapore team. The role suits someone who enjoys solving complex challenges and building AI-driven solutions that help clients improve efficiency, customer experience and compliance in insurance.

Key responsibilities

• Build multi-agent workflows to optimise business processes within insurance.
• Develop and maintain software pipelines for data processing, model training and deployment using Python-based frameworks.
• Train, fine-tune and optimise neural network models, including LLMs, for use cases such as risk assessment and customer query resolution.
• Work with data scientists, engineers and business stakeholders to turn requirements into production-ready AI applications.
• Ensure AI models comply with banking regulations, data privacy standards (e.g. GDPR, CCPA) and ethical AI practices.
• Monitor deployed solutions in production, troubleshoot issues and apply updates.
• Take part in code reviews, agile ceremonies and CI/CD processes.

Required profile

• 3-12 years of professional experience in AI/ML engineering, with a focus on software development and deployment.
• Experience building and integrating components for LLM, RAG, search, agent-based and automation solutions.
• Track record of hands-on model training and fine-tuning, with a focus on performance and scalability.
• Proven expertise with LLMs (e.g. GPT, BERT) and agentic solutions using frameworks such as LangChain, LangGraph or ADK.
• Unrestricted working rights in Singapore; only Singapore citizens and permanent residents are considered, and employment pass sponsorship is not available.

Required skills

• Strong software engineering with Python, Java, React Next and Node.js.
• Familiarity with cloud platforms (AWS, Azure or Google Cloud) for model deployment and data management.
• Solid understanding of machine learning concepts, including supervised/unsupervised learning, evaluation metrics and bias mitigation.
• Version control (Git) and agile methodologies.
• Preferred: experience in financial services, big data tools (Spark, Hadoop), SQL/NoSQL databases, graph databases, DevOps, Docker, Kubernetes and MLOps.