Job Description
Location
Singapore
About The Role
We are looking for a Full Stack Engineer – Generative AI & Agentic AI to design, develop, and deliver modern enterprise applications while leveraging Generative AI and Agentic AI capabilities across the Software Development Lifecycle (SDLC). This role combines deep full-stack engineering expertise with AI-native development practices to accelerate delivery, improve quality, and drive business outcomes through intelligent automation and innovation.
Key Responsibilities
Design, develop, test, deploy, and support end-to-end applications across frontend, backend, APIs, databases, and cloud environments.
Leverage Generative AI and Agentic AI technologies to accelerate requirements analysis, solution design, development, testing, deployment, and ongoing operations.
Build and integrate AI-powered solutions, including Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG) architectures, intelligent agents, and workflow automation.
Utilize AI-assisted development tools and coding copilots to improve engineering productivity, code quality, and delivery efficiency.
Develop scalable APIs, microservices, and cloud-native solutions that meet performance, reliability, and security requirements.
Implement automated testing frameworks, CI/CD pipelines, Infrastructure as Code (IaC), and DevSecOps practices to support high-quality software delivery.
Apply AI-driven observability and AIOps techniques to monitor application health, proactively identify issues, and optimize system performance.
Collaborate with cross-functional teams, including architects, product owners, AI specialists, and business stakeholders, to deliver innovative technology solutions.
Required Qualifications
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline, or equivalent practical experience.
Proven experience in full-stack software development using technologies such as React, Angular, Java, .NET, Node.js, Python, or similar modern frameworks.
Strong experience designing and developing RESTful APIs, microservices, and database-driven applications.
Experience with cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform (GCP).
Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
Knowledge of CI/CD pipelines, Infrastructure as Code (Terraform, Bicep, or equivalent), and DevSecOps practices.
Understanding of Generative AI concepts, Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG).
Strong analytical, problem-solving, and software engineering skills, with a focus on scalability, maintainability, and security.
Preferred Qualifications
Experience building production-grade AI applications using frameworks such as LangGraph, CrewAI, Semantic Kernel, LangChain, AutoGen, or similar agent orchestration platforms.
Familiarity with vector databases, embeddings, knowledge retrieval systems, and AI application architecture patterns.
Experience with AI observability, model evaluation, responsible AI, and AI governance practices.
Professional certifications in cloud technologies, software engineering, DevOps, or artificial intelligence.
