Full stack ai engineer

The Full Stack AI Engineer is responsible for designing, developing, and deploying enterprise-grade AI applications that transform business requirements into secure, scalable, and production-ready digital solutions. This role combines full-stack software engineering with AI integration, retrieval-augmented generation (RAG), intelligent agents, workflow automation, and enterprise system integration to deliver innovative AI-powered products.

Key Responsibilities 1. Full-Stack Application Development Design, develop, and maintain responsive, enterprise-grade web applications using modern frontend technologies. Develop backend services, REST APIs, databases, and integration layers for AI-enabled applications. Translate business requirements, user stories, and UI/UX designs into scalable software solutions. Implement secure authentication, authorization, role-based access control (RBAC), audit logging, and data protection mechanisms. Produce clean, maintainable, testable, and well-documented code following engineering best practices. 2. AI Solution Development Integrate Large Language Models (LLMs) into enterprise applications using secure prompting techniques and orchestration frameworks. Develop Retrieval-Augmented Generation (RAG) solutions utilizing enterprise knowledge repositories. Build AI agents capable of retrieving information, executing workflows, interacting with enterprise systems, and escalating tasks when required. Design prompt templates, guardrails, evaluation methods, and feedback mechanisms to improve AI performance. Develop reusable AI components and frameworks to accelerate future solution delivery. 3. Business Solution Delivery Deliver AI-powered Minimum Viable Products (MVPs), pilot solutions, and production-ready applications within agreed timelines. Develop AI solutions supporting business functions such as: HR automation Finance automation Intelligent knowledge assistants Software development lifecycle (SDLC) acceleration Automated testing Internal AI copilots Workflow automation Collaborate with business stakeholders to refine requirements and continuously improve user experience. Integrate AI applications with enterprise systems, APIs, document repositories, communication platforms, and business applications. 4. Quality Assurance & Testing Develop unit, integration, and AI-specific regression tests. Monitor application performance, latency, reliability, cost, and AI response quality. Resolve production issues and support application maintenance. Ensure compliance with security, governance, and data privacy standards. Prepare technical documentation, deployment guides, and operational runbooks. 5. Collaboration & Continuous Improvement Work closely with business users, architects, designers, platform teams, and security teams. Participate in solution design workshops and technical reviews. Share knowledge, reusable code, and engineering best practices across the development team. Support demonstrations, user training, and adoption of AI solutions. Technical Skills

The ideal candidate should possess experience with:

Frontend Development React Next.js Type Script HTML5 CSS3 Modern UI component libraries Backend Development Python Fast API Node.js Nest JS Express.js API & Integration REST APIs Graph QL Webhooks Event-driven architecture Databases Postgre SQL SQL Server Cosmos DB Other relational or No SQL databases AI Technologies Open AI APIs or equivalent LLM platforms Azure AI services or similar cloud AI platforms Retrieval-Augmented Generation (RAG) Embeddings Vector databases Prompt engineering AI evaluation techniques AI Frameworks Lang Chain Lang Graph Semantic Kernel Similar orchestration frameworks Security OAuth JWT Role-Based Access Control (RBAC) Secure API design Enterprise authentication Dev Ops Git Git Hub Azure Dev Ops CI/CD pipelines Automated testing Qualifications & Experience Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, or a related discipline. Minimum 5 years of experience in full-stack software development or AI application engineering. Minimum 2 years of experience developing cloud-based enterprise applications. Experience integrating APIs and third-party services. Experience developing secure enterprise applications with authentication and authorization. Demonstrated experience delivering software in Agile environments. Experience working with Large Language Models (LLMs), AI assistants, chatbots, automation platforms, or Retrieval-Augmented Generation (RAG). Preferred Experience

Experience in one or more of the following areas is advantageous:

Financial Services Banking Telecommunications Enterprise Technology Cloud platforms Workflow automation Knowledge management Document processing Test automation Conversational AI Voice and IVR solutions Responsible AI and secure AI implementation Core Competencies Strong software engineering fundamentals AI application development expertise Analytical thinking and structured problem-solving Excellent communication and stakeholder management Strong collaboration and teamwork Ability to work independently Delivery-focused mindset Adaptability and continuous learning Innovation and creativity Strong attention to detail Ability to work under pressure and manage multiple priorities Key Performance Indicators (KPIs)

The role will be measured on:

Delivery of AI products within agreed timelines Quality and maintainability of software solutions User adoption and business value delivered AI solution accuracy and reliability Successful integration with enterprise systems Application performance, stability, and scalability Compliance with security and governance standards