Job title: Senior Generative AI Application Engineer
Position level: Professional / Senior Executive
Employment type: Contract , Contract duration: 12 months renewable
Working location : Singapore CBD in 2026; PDD from 2027
Our client is a leading bank in Asia with an established international network across Asia Pacific, Europe and North America.
This is a hands-on engineering position operating at the intersection of software engineering, Generative AI application development, enterprise integration and production delivery.
This is not a pure research, data science or prompt-engineering-only role. The successful candidates will design, build, deploy and enhance production-grade GenAI applications that are reliable, observable, secure, maintainable and useful to enterprise users.
Key Responsibilities
- Design, develop and enhance production-grade Generative AI applications.
- Build GenAI solutions using LangGraph, LangChain or comparable orchestration frameworks.
- Develop retrieval-augmented generation pipelines, agentic workflows, tool-calling capabilities, prompt orchestration and context-management solutions.
- Integrate hosted and open-weight large language models into enterprise applications.
- Connect GenAI applications with APIs, backend services, databases, document repositories, enterprise data sources and operational platforms.
- Build reliable backend services using Python, Java or comparable programming languages.
- Design and implement model-routing, retrieval, caching, conversation-state and fallback mechanisms.
- Establish application logging, tracing, monitoring, evaluation and troubleshooting capabilities.
- Develop automated evaluation frameworks to measure retrieval quality, response accuracy, reliability and application performance.
- Deploy and operate GenAI workloads within Kubernetes, OpenShift or similar containerised environments.
- Apply appropriate security, access-control and data-protection measures to enterprise AI solutions.
- Troubleshoot complex application, model, integration and infrastructure issues.
- Write clean, maintainable, reusable and testable production code.
- Review technical designs, challenge unsuitable approaches and recommend practical improvements.
- Collaborate with business, application, data, platform, infrastructure, architecture and cybersecurity teams.
- Support applications through development, testing, deployment, production stabilisation and continuous improvement.
Requirements
- At least 5 years of software engineering, application development or closely related technical experience.
- Recent hands-on experience designing and building Generative AI applications.
- Proven experience delivering production-grade GenAI applications—not only demonstrations, proofs of concept or academic projects.
- Strong practical experience with LangGraph, LangChain or comparable LLM orchestration frameworks.
- Hands-on experience with RAG, agentic workflows, tool calling, prompt orchestration and context management.
- Experience integrating LLMs with real applications through APIs, backend services and enterprise data sources.
- Strong backend engineering skills using Python, Java or similar languages.
- Good understanding of REST APIs, distributed systems and production-resilience patterns.
- Experience designing retrieval pipelines involving document parsing, chunking, embeddings, vector search, reranking and cited generation.
- Practical experience with observability, logging, tracing, evaluation and troubleshooting for GenAI or backend applications.
- Familiarity with Docker and container orchestration platforms such as Kubernetes or OpenShift.
- Understanding of application security, access controls and responsible handling of enterprise information.
- Strong analytical, debugging and problem-solving capabilities.
- Ability to work effectively with incomplete information and evolving requirements.
- Strong ownership, curiosity and genuine interest in building useful AI products.
- Good communication skills and the confidence to challenge weak technical designs.
- Ability to coordinate effectively across business and multidisciplinary technology teams.
Banking or financial-services experience is advantageous but not mandatory. Strong GenAI application engineering and practical production-delivery experience are more important.
Good-to-Have Experience
- Open-weight models such as Llama, Mistral, Qwen or comparable models.
- vLLM or similar model inference and serving frameworks.
- DeepAgent or comparable agent frameworks.
- Langfuse or similar LLM observability and evaluation platforms.
- Elasticsearch or similar enterprise search technologies.
- Redis for caching, conversation state, rate limiting, pub/sub or queue-backed workflows.
- Vector databases and hybrid retrieval approaches.
- Model Context Protocol integrations.
- Cloud-based GenAI services and deployment platforms.
- CI/CD pipelines for AI and backend applications.
Interested candidates are kindly requested to email their CV with their experience to [email protected]
We look forward to your application!