AI-Enabled Full Stack Engineer

We are seeking a talented AI-Enabled Full Stack Developer to join us. This role combines strong full stack engineering expertise 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 capabilities into the products you build. You will help the team raise engineering productivity and quality through smart, responsible use of AI, and contribute to shaping how the company delivers AI-enabled software to our clients.ResponsibilitiesFull Stack Application DevelopmentDesign, develop, test, and deploy end-to-end web applications spanning frontend, backend services, APIs, and data layersTranslate functional and non-functional requirements into well-architected, maintainable software components using established design patternsBuild and maintain microservices and RESTful / GraphQL APIs using modern stacks such as Java/Spring Boot, .NET, Python, or Node.jsDevelop 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 patternsContribute to architecture discussions and trade-off analyses for performance, scalability, security, and costAI-Assisted Software EngineeringUse 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 workflowLeverage AI tools to accelerate coding, refactoring, code review, unit test generation, test data creation, and documentationApply prompt engineering and context-design techniques to get high-quality, trustworthy outputs from AI coding assistantsCritically review AI-generated code for correctness, security, performance, licensing, and alignment with project and company coding standards before committingMeasure and communicate the productivity and quality impact of AI-assisted workflows on project deliveryAI Solution Integration& DeliveryIntegrate 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 workflowsBuild against foundation model APIs and managed AI services such as Azure OpenAI, Amazon Bedrock, Google Vertex AI / Gemini, Anthropic Claude, and OpenAIWork with vector databases and AI application frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel; pgvector, Pinecone, Weaviate, FAISS) to deliver context-aware experiencesDesign AI features with clear evaluation criteria, guardrails, and human-in-the-loop checkpointsPartner with data scientists, AI/ML engineers, and solution architects to take AI capabilities from prototype to productionContribute to pre-sales and client engagements by prototyping AI-enabled features and shaping practical, value-driven solution designsQuality Engineering& Secure CodingFollow secure coding principles and company security guidelines to prevent common vulnerabilities across frontend, backend, and AI integrationsWrite and maintain unit, integration, and end-to-end tests; meet project and organisation test coverage targetsPerform static code analysis, code reviews, and threat-aware reviews of AI-generated code and AI-integrated featuresAddress defects, performance issues, and production incidents through disciplined root-cause analysisDevOps & Continuous DeliveryAdopt Agile, DevOps, and CI/CD practices to deliver software iteratively and reliablyBuild and maintain pipelines (e.g., Jenkins, GitLab CI) for automated build, test, and deploymentContainerise 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 telemetryCollaboration &Knowledge SharingPartner with business analysts, designers, data scientists, and product owners to translate user needs into working softwareParticipate in and lead peer reviews, design reviews, and knowledge-sharing sessionsMentor junior developers on full stack engineering fundamentals and effective, responsible use of AI toolsDocument designs, APIs, and AI-integration patterns in a clear and reusable wayResponsible AI &Engineering ExcellenceChampion responsible use of AI tools, including IP protection, client data handling, confidentiality, and licence-compliance considerationsHelp define and uphold team guardrails and standards for AI-assisted development and AI-integrated productsStay current with the rapidly evolving AI tooling, model, and framework landscape, and bring relevant advances back to the teamContribute to company assets, reusable components, and reference implementations that accelerate future AI-enabled deliveryQualificationsDiploma, Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or a related field; relevant coding certifications are also welcome3 years of professional experience building and delivering production web applications as a full stack developerStrong 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/CSS3Solid understanding of API design (REST, JSON; GraphQL a plus), microservices, event-driven patterns, and integration with third-party systemsExperience 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 toolchainsWorking 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 speedWorking knowledge of prompt engineering and how to structure effective context and instructions for AI coding assistantsFamiliarity 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 plusExposure 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 plusAwareness of responsible AI principles, data privacy, IP considerations, and security implications of using AI tools on client engagementsStrong problem-solving, analytical thinking, and sound judgement in when - and when not - to rely on AI-generated outputExcellent written and spoken English, with strong communication and collaboration skills in a team environmentSelf-motivated, customer-focused, and committed to high engineering and delivery standardsInterested candidates may send their CV to MAC (Reg No. R1221300) [email protected] quoting the job title in the Subject line. We regret that only shortlisted candidates will be notified.