M40 - Full Stack Engineer (AI)

Role OverviewWe are seeking a Full Stack & AI Engineer to design, develop, deploy, and maintain secure, scalable, and high-performance applications across frontend, backend, cloud, and AI platforms. The role involves full-stack application development, maintenance of the Chatbook Application, and the development and integration of enterprise-grade AI solutions.Key ResponsibilitiesFull Stack Application DevelopmentDesign, develop, deploy, and maintain secure, scalable, and high-performance web applications across frontend, backend, and cloud platforms.Ensure application quality, performance, security, reliability, and maintainability through modern software engineering practices.Work in an Agile and DevOps-driven environment, applying CI/CD, automation, testing, and secure development practices.Collaborate with cross-functional teams to deliver reliable and production-ready software solutions.Chatbook Application MaintenanceTake ownership of the Chatbook Application following knowledge transfer.Provide ongoing application maintenance, enhancements, performance optimisation, troubleshooting, and incident resolution.Ensure application stability, reliability, and continuous improvement.AI Solution Development & IntegrationDesign and implement AI-powered solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic frameworks, and Model Context Protocol (MCP)-based integrations.Develop enterprise AI workflows and production-ready AI applications.Implement monitoring, observability, evaluation, and guardrails for AI solutions.Translate business requirements into scalable, secure, and production-ready AI solutions.RequirementsEducationDegree or Diploma in Computer Science, Computer Engineering, Electronics Engineering, Information Technology, or a related discipline.AI & Agentic EngineeringHands-on experience building AI agents and multi-agent systems using frameworks such as LangChain, LlamaIndex, LangGraph, AutoGen, or CrewAI.Strong understanding of agentic design patterns, including planning, tool use, memory, and reflection.Experience with MCP-based tool integrations and maintaining GenAI and agentic applications in production environments.Experience implementing AI guardrails, application evaluation, and production monitoring.Knowledge of LLMOps practices, including prompt management, model evaluation, LLM observability tools such as LangSmith or similar platforms, AI governance, and responsible AI principles.Software EngineeringStrong software engineering fundamentals, including clean code, design patterns, automated testing, REST API development, containerisation, container orchestration, CI/CD pipelines, and Git-based version control.Proficiency in Next.js, React, Python, TypeScript, and Tailwind CSS.Strong experience with relational and NoSQL databases, including query optimisation, schema design, and data management.Familiarity with vector databases for RAG and semantic search.Knowledge of secure software development practices, including authentication and authorisation, API security, secrets management, and OWASP best practices.Cloud & InfrastructureHands-on experience with AWS cloud services, including Lambda, API Gateway, S3, CloudWatch, and IAM.Experience with AWS Bedrock for accessing and orchestrating foundation models such as Anthropic Claude.Familiarity with AWS Bedrock capabilities such as Knowledge Bases, Agents, and Guardrails for production-grade generative AI applications.Experience developing and deploying applications on Government Commercial Cloud (GCC) is an advantage.