GenAI Application Engineer (LLM / RAG / Agentic AI)

Client: Bank Sector Client Located In Singapore LLM / RAG / Agentic AI / LangGraph / LangChain Role Overview We are looking for a Senior GenAI Application Engineer to design, build and deliver production-grade Generative AI (GenAI) and Large Language Model (LLM) applications for enterprise environments. This is a hands-on GenAI application engineering / software engineering role focused on building real-world AI applications rather than pure AI research or prompt engineering. You will work across LLM application development, Retrieval-Augmented Generation (RAG), Agentic AI, AI agents, LangGraph/LangChain orchestration, backend engineering, enterprise APIs and production deployment . The successful candidate should have strong software engineering fundamentals and practical experience taking GenAI / LLM applications from prototype or Proof of Concept (POC) into production . Banking or financial services experience is not required. Key Responsibilities • Design, develop and enhance production-grade GenAI / LLM applications used by enterprise users. • Build Agentic AI / AI Agent workflows using frameworks such as LangGraph, LangChain or similar LLM orchestration frameworks . • Develop Retrieval-Augmented Generation (RAG) solutions including retrieval workflows, context management and prompt orchestration. • Build applications involving: • Large Language Models ( LLMs ) • Retrieval-Augmented Generation ( RAG ) • AI Agents / Agentic AI • Tool calling / function calling • Prompt orchestration • Context management • Integrate LLM applications with enterprise systems, REST APIs, backend services, databases, enterprise data sources and operational platforms . • Work with both open-weight / open-source models and hosted LLM services . • Support model integration and LLM inference / model-serving architectures . • Develop reliable backend services using Python, Java or similar programming languages . • Design scalable APIs and services for enterprise GenAI applications. • Implement production engineering practices including: • Logging • Monitoring • Distributed tracing • LLM observability • Evaluation / LLM evaluation • Error handling • Fallback mechanisms • Debugging and troubleshooting • Deploy and support GenAI applications within containerized environments such as Kubernetes or OpenShift . • Work closely with application, data, platform, infrastructure, DevOps, security and business teams. • Review technical designs, identify weaknesses and recommend practical improvements. • Ensure solutions are scalable, reliable, maintainable, observable and production-ready . Key Requirements Software Engineering • 6+ years of software engineering experience , preferably with recent hands-on experience developing GenAI / LLM applications. • Strong backend software development experience using Python, Java or similar languages . • Strong understanding of: • REST APIs / API development • Backend services • Distributed systems • Enterprise application integration • Scalability and resilience • Production application architecture • Ability to write clean, maintainable and testable production code. Generative AI / LLM Engineering Proven hands-on experience building and deploying production-grade Generative AI applications , beyond simple prototypes, demos or hackathons. Strong practical knowledge of: • Generative AI / GenAI • Large Language Models (LLMs) • LangGraph • LangChain • Retrieval-Augmented Generation (RAG) • Agentic AI • AI Agents / Agent workflows • LLM orchestration • Tool calling / function calling • Prompt engineering / prompt orchestration • Context management • LLM application integration Candidates should understand how these technologies are used together to build reliable enterprise AI applications. Production GenAI Engineering Experience implementing production engineering practices for GenAI or backend applications, including: • LLM observability • Logging and monitoring • Distributed tracing • LLM / application evaluation • Failure handling • Fallback mechanisms • Debugging and troubleshooting • Application reliability • Performance and scalability Experience taking AI applications from POC / prototype through production deployment is particularly important. Open-Weight Models & Model Serving Experience working with or integrating: • Open-weight models / open-source LLMs • Hosted LLM APIs • Model-serving platforms • LLM inference services Hands-on exposure to vLLM or similar LLM inference / model-serving frameworks would be advantageous. Cloud / Containers / Platform Engineering Familiarity with production deployment environments such as: • Kubernetes • OpenShift • Containerized application deployment • Cloud or enterprise infrastructure environments Candidates should be comfortable collaborating with DevOps, platform, infrastructure and security teams when deploying GenAI applications. Nice to Have Experience with one or more of the following would be advantageous: • Langfuse or similar LLM observability platforms • Elastic / Elasticsearch • Redis • vLLM • DeepAgent or similar Agentic AI frameworks • Cloud-based GenAI deployments • LLM model serving / inference • Caching and conversation-state management • Queue-backed AI workflows • Low-latency GenAI application architectures What We Are Looking For The ideal candidate is a hands-on software engineer who has moved into Generative AI application development , rather than someone focused purely on AI research or prompt engineering. You should be able to: • Build real enterprise GenAI applications • Design reliable RAG and Agentic AI architectures • Integrate LLMs with APIs and enterprise systems • Write production-quality backend code • Diagnose complex technical problems • Challenge weak technical designs • Work across application, data, platform, infrastructure and security teams • Balance engineering quality with pragmatic delivery We value engin…