AI Full Stack Developer 12 months contract

Key Responsibilities Full Stack Application Development Design, develop, test, and deploy end-to-end web applications spanning frontend, backend services, APIs, and data layers Translate functional and non-functional requirements into well-architected, maintainable software components using established design patterns Build and maintain microservices and RESTful / GraphQL APIs using modern stacks such as Java/Spring Boot, .NET, Python, or Node.js Develop responsive, accessible user interfaces using modern JavaScript/TypeScript frameworks (e.g., React, Angular, Vue) Model data and work with both SQL and NoSQL databases design efficient queries, schemas, and integration patterns Contribute to architecture discussions and trade-off analyses for performance, scalability, security, and cost AI-Assisted Software Engineering Use AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code, Amazon Q Developer, Gemini Code Assist, JetBrains AI Assistant, or equivalent) as a daily part of the development workflow Leverage AI tools to accelerate coding, refactoring, code review, unit test generation, test data creation, and documentation Apply prompt engineering and context-design techniques to get high-quality, trustworthy outputs from AI coding assistants Critically review AI-generated code for correctness, security, performance, licensing, and alignment with project and NCS coding standards before committing Measure and communicate the productivity and quality impact of AI-assisted workflows on project delivery AI Solution Integration & Delivery Integrate AI and Generative AI capabilities into enterprise applications - including LLM-powered features, Retrieval-Augmented Generation (RAG), chatbots and virtual assistants, intelligent document processing, recommendations, and agentic workflows Build against foundation model APIs and managed AI services such as Azure OpenAI, Amazon Bedrock, Google Vertex AI / Gemini, Anthropic Claude, and OpenAI Work with vector databases and AI application frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel pgvector, Pinecone, Weaviate, FAISS) to deliver context-aware experiences Design AI features with clear evaluation criteria, guardrails, and human-in-the-loop checkpoints Partner with data scientists, AI/ML engineers, and solution architects to take AI capabilities from prototype to production Contribute to pre-sales and client engagements by prototyping AI-enabled features and shaping practical, value-driven solution designs Quality Engineering & Secure Coding Follow secure coding principles and NCS security guidelines to prevent common vulnerabilities across frontend, backend, and AI integrations Write and maintain unit, integration, and end-to-end tests meet project and organisation test coverage targets Perform static code analysis, code reviews, and threat-aware reviews of AI-generated code and AI-integrated features Address defects, performance issues, and production incidents through disciplined root-cause analysis DevOps & Continuous Delivery Adopt Agile, DevOps, and CI/CD practices to deliver software iteratively and reliably Build and maintain pipelines (e.g., Jenkins, GitLab CI) for automated build, test, and deployment Containerise applications and deploy to container platforms (Docker, Kubernetes) and cloud environments (AWS, Azure, GCP) Instrument applications for observability - logs, metrics, traces, and, where applicable, AI-feature evaluation telemetry Collaboration & Knowledge Sharing Partner with business analysts, designers, data scientists, and product owners to translate user needs into working software Participate in and lead peer reviews, design reviews, and knowledge-sharing sessions Mentor junior developers on full stack engineering fundamentals and effective, responsible use of AI tools Document designs, APIs, and AI-integration patterns in a clear and reusable way Responsible AI & Engineering Excellence Champion responsible use of AI tools, including IP protection, client data handling, confidentiality, and licence-compliance considerations Help define and uphold team guardrails and standards for AI-assisted development and AI-integrated products Stay current with the rapidly evolving AI tooling, model, and framework landscape, and bring relevant advances back to the team Contribute to NCS assets, reusable components, and reference implementations that accelerate future AI-enabled delivery Qualifications Diploma, Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or a related field relevant coding certifications are also welcome 2 to 6 years of professional experience building and delivering production web applications as a full stack developer Strong hands-on proficiency in at least one backend stack: Node.js (Express, NestJS), Golang, Python. Strong hands-on proficiency in at least one modern frontend framework: React (Next.js a plus), Angular, or Vue.js, with solid fundamentals in JavaScript/TypeScript, HTML5, and CSS/CSS3 Solid understanding of API design (REST, JSON GraphQL a plus), microservices, event-driven patterns, and integration with third-party systems Experience with SQL and NoSQL databases (e.g., Oracle, MS SQL Server, PostgreSQL, MySQL, MongoDB, Redis) Experience with Git-based source control, Agile delivery, test-driven development, and CI/CD toolchains Working experience with containers and cloud platforms (Docker, Kubernetes AWS, Azure, or GCP) Demonstrated day-to-day use of AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code, Amazon Q Developer, Gemini Code Assist, JetBrains AI Assistant, Tabnine, Windsurf, or equivalent) and ability to articulate how these tools have improved your delivery quality and speed Working knowledge of prompt engineering and how to structure effective context and instructions for AI coding assistants Familiarity with Large Language Models and Generative AI concepts - tokens, context windows, embeddings, vector search, and Retrieval-Augmented Generation (RAG) Exposure to at least one LLM / GenAI platform or SDK (OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, Google Vertex AI / Gemini, Hugging Face) Involvement in at least one project that delivered an AI solution capability - such as a GenAI / LLM-powered application, RAG system, chatbot or virtual assistant, intelligent document processing, ML-powered feature, or AI agent - is a strong plus Exposure to AI application frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel), vector databases (e.g., Pinecone, Weaviate, pgvector, FAISS), agentic patterns, tool/function calling, Model Context Protocol (MCP), and AI evaluation or guardrail practices is a plus Awareness of responsible AI principles, data privacy, IP considerations, and security implications of using AI tools on client engagements By sending us your personal data, you are deemed to consent to PERSOL Tech Services Pte Ltd ('PERSOL') and its local and overseas subsidiaries and affiliates to collect, use and disclose your personal data for the purposes set out in the Privacy Policy.

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