Full-Stack Software Engineer (GenAI Platforms) (4-7 Years Experience, 1 Year Contract extendable)

RHINO PARTNERS PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Rhino Partners, a Singapore-headquartered technology consultancy, seeks a seasoned Full-Stack Software Engineer to build AI-powered platforms. You will design, develop, and evolve modern applications with GenAI, semantic search, and cloud-native architectures.

You will collaborate with Product Managers, Architects, UX Designers, and engineers to deliver scalable, secure, and production-ready solutions, while mentoring juniors and promoting engineering best practices.

Qualifications

• 5+ years of full-stack software development experience.
• Strong hands-on with Node.js, TypeScript, and modern JS frameworks.
• Experience with RESTful APIs and microservices.
• Cloud platform experience (AWS, Azure, or Google Cloud).
• SQL and NoSQL databases knowledge.
• CI/CD pipelines and containerised apps.
• Good practices: Clean Code, TDD, code reviews, Agile.

Responsibilities

• Design, develop, test, and maintain scalable full-stack web apps.
• Build backend services, APIs, and frontend apps for AI-powered experiences.
• Contribute to system architecture, mentoring, and engineering standards.
• Implement CI/CD pipelines and cloud-native architectures.
• Collaborate with PMs, architects, UX designers, and engineers.

Skills

Full-stack development

Node.js

TypeScript

React/Next.js

RESTful APIs

CI/CD

Cloud platforms

Agile

Job description

About Rhino Partners

Rhino Partners is a Singapore-headquartered technology consultancy that helps organisations design, build, and scale modern digital platforms. We specialise in custom software development, cloud-native engineering, data platforms, and AI-enabled solutions across government, financial services, insurance, and enterprise sectors.

We are seeking an experienced Full-Stack Software Engineer to join a high-impact team building next-generation digital platforms powered by Generative AI, intelligent search, and modern cloud architectures.

This role offers the opportunity to work on large-scale systems that serve thousands of users while helping shape the responsible adoption of AI in production environments.

The Role

As a Full-Stack Software Engineer (GenAI Platforms), you will play a key role in designing, developing, and evolving AI-powered applications that combine traditional software engineering with modern search and Generative AI capabilities.

You will work closely with Product Managers, Architects, UX Designers, and fellow Engineers to deliver scalable, secure, and production-ready solutions. Beyond hands-on development, you will contribute to architectural decisions, mentor team members, and champion engineering best practices across the software delivery lifecycle.

Key Responsibilities

Software Engineering & Solution Development

• Design, develop, test, and maintain scalable full-stack web applications using modern technologies and frameworks.
• Build and integrate backend services, APIs, and frontend applications that support AI-powered user experiences.
• Develop solutions leveraging:

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Semantic Search
• Vector Databases
• Knowledge Retrieval Systems

• Integrate external AI services such as OpenAI, Azure OpenAI, Anthropic Claude, or similar platforms.
• Design and implement secure, performant, and resilient cloud-native architectures.
• Participate in technical design discussions and contribute to system architecture decisions.

AI & Search Engineering

• Build and optimise search experiences using modern search technologies and retrieval techniques.
• Design and implement RAG pipelines and document retrieval frameworks.
• Evaluate and improve prompt engineering strategies, model performance, and response quality.
• Establish AI evaluation frameworks to measure accuracy, relevance, latency, and cost efficiency.
• Implement monitoring, observability, and guardrails for production AI systems.
• Contribute to responsible AI practices, including governance, security, and data privacy considerations.

Cloud, DevOps & Quality Engineering

• Develop and maintain CI/CD pipelines to support automated build, testing, and deployment processes.
• Contribute to infrastructure automation and cloud platform engineering initiatives.
• Implement and maintain automated testing strategies, including:

• Unit Testing
• Integration Testing
• End-to-End Testing
• Performance Testing

• Troubleshoot production issues and participate in incident resolution activities.
• Continuously improve system reliability, scalability, and operational efficiency.

Collaboration & Technical Leadership

• Collaborate with Product Owners, Business Analysts, Designers, and stakeholders to translate business requirements into technical solutions.
• Participate in Agile ceremonies including sprint planning, backlog refinement, reviews, and retrospectives.
• Provide technical guidance and mentorship to junior engineers.
• Contribute to engineering standards, coding practices, and technical documentation.
• Stay current with emerging technologies and evaluate their applicability to business and public-sector use cases.

Requirements

Essential Skills & Experience

• 5+ years of experience in full-stack software development.
• Strong hands-on experience with:

• Node.js
• TypeScript
• Modern JavaScript frameworks (React, Next.js, or similar)
• RESTful APIs and microservices architectures

• Experience working with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong understanding of SQL and NoSQL databases.
• Experience building and maintaining CI/CD pipelines.
• Solid understanding of software engineering best practices, including:

• Clean Code Principles
• Test-Driven Development (TDD)
• Code Reviews
• Agile Delivery Practices

• Experience developing and deploying containerised applications.

AI & Search Experience

• Experience designing or implementing solutions involving:

• Search Platforms
• Semantic Search
• Vector Search
• Information Retrieval Systems

• Hands-on experience integrating LLMs into production applications.
• Familiarity with:

• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Embedding Models
• Model Evaluation Techniques

• Understanding of AI system trade-offs including:

• Latency vs Cost
• Accuracy vs Hallucination Risk
• Retrieval Quality vs Performance

• Knowledge of AI governance, secu