Full Stack Engineer – Generative AI & Agentic AI
Location
About the Role
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.
Singapore
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