Senior Generative AI Application Engineer_ Contract

ntt singapore pte. ltd.; Singapore; On-site; SGD 100,000 - 180,000; Full time

Job summary

A senior-level role at a leading bank in Asia, focusing on building and delivering production-grade GenAI applications that integrate with enterprise systems.

The role requires hands-on experience with GenAI frameworks, API integrations and end-to-end production deployment. You will develop scalable pipelines, ensure security and observability, and collaborate with cross-functional teams to deliver enterprise-grade GenAI solutions.

Qualifications

• 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 demos or POCs.
• 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.

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 and enterprise data sources.
• 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 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 and propose practical improvements.
• Collaborate with business, application, data, platform, infrastructure, architecture and cybersecurity teams.
• Support applications through development, testing, deployment, production stabilisation and continuous improvement.

Skills

GenAI development

Backend development

API integration

Observability

CI/CD

Tools

LangGraph

LangChain

Docker

Kubernetes

OpenShift

REST APIs

Job description

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.