AI-Enabled Full Stack Engineer

ANCHOR GLOBAL CONSULTING PTE. LTD.; Singapore; On-site; SGD 90,000 - 135,000; Full time

Job summary

ANCHOR GLOBAL CONSULTING PTE. LTD. is seeking a capable Front to Back Software Engineer to design, develop, and deploy modern digital solutions. You will work across the full stack from UI to APIs, leveraging AI-assisted coding tools and integrating AI capabilities into product features.

The role emphasizes rapid delivery, scalable architecture, and collaboration with data scientists and solution architects to turn prototypes into production-ready software.

Qualifications

• 3 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, Golang, Python.
• Strong hands-on proficiency in at least one frontend framework: React/Angular/Vue.
• Solid understanding of API design (REST/JSON; GraphQL a plus) and microservices.
• Experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB).

Responsibilities

• Design, develop, test, and deploy end-to-end web applications across frontend, backend, APIs, and data layers.
• Translate requirements into well-architected software components using design patterns.
• Develop microservices and REST/GraphQL APIs using modern stacks.
• Build responsive, accessible UIs with modern JS frameworks.
• Collaborate with data scientists and architects to deliver AI-enabled features.

Skills

Full stack development

AI-assisted tools

Java/Spring Boot

React/Angular/Vue

SQL/NoSQL

CI/CD

Docker/Kubernetes

Cloud platforms

REST/GraphQL

TypeScript

Education

Bachelor's/Master's in CS/Engineering

Tools

GitHub Copilot

Cursor

Claude Code

JetBrains AI Assistant

Tabnine

Job description

This role combines strong full stack engineering with hands‑on fluency in AI‑assisted development tools to design, build, and deliver modern digital solutions at speed and scale. You'll work across the entire application stack -from intuitive user interfaces to robust APIs and data services - while leveraging AI coding assistants and, where appropriate, integrating AI capabilitiesinto the products you build. You will help the team raise engineeringproductivity and quality through smart, responsible use of AI, and contributeto shaping how the company delivers AI‑enabled software to our clients.

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 company 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 company 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 & ContinuousDelivery

• 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 company 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
• 3 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, Llama Index, 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
• Strong problem‑solving, analytical thinking, and sound judgement in when - and when not - to rely on AI‑generated output
• Excellent written and spoken English, with strong communication and collaboration skills in a team environment
• Self‑motivated, customer‑focused, and committed to high engineering and delivery standards