Full stack ai engineer (ai automation, rag and workflow applications)

The Full Stack AI Engineer is responsible for designing, developing, and delivering secure, scalable, and enterprise-grade AI applications that transform business requirements into practical digital solutions. This role combines modern full-stack software engineering with Artificial Intelligence (AI), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent agents, workflow automation, and enterprise system integration.

The successful candidate will build AI-powered applications from concept through production while collaborating with cross-functional teams to deliver innovative, reliable, and business-focused solutions.

Key Responsibilities 1. Full Stack Application Development Design, develop, and maintain responsive, accessible, and enterprise-ready 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, well-tested, and well-documented code in accordance with engineering best practices. 2. AI Solution Development Integrate Large Language Models (LLMs) into enterprise applications using secure prompt engineering 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 where appropriate. Design reusable prompt templates, guardrails, evaluation methods, and feedback mechanisms. Collaborate with AI architects to implement scalable AI design patterns and reusable components. 3. Enterprise Solution Delivery Develop and deliver AI-powered applications from concept through pilot and production deployment. Build solutions supporting business functions such as: Intelligent knowledge assistants HR automation Finance automation Workflow automation Software development lifecycle (SDLC) acceleration AI-powered testing solutions Internal AI copilots Conversational AI applications Integrate applications with enterprise systems, APIs, document repositories, communication platforms, and business applications. Gather user feedback and continuously improve application performance and user experience. 4. Quality Assurance & Testing Develop unit, integration, and AI-specific regression tests. Monitor application performance, latency, accuracy, reliability, and operational costs. Troubleshoot production issues and provide ongoing application support. Ensure secure handling of sensitive information and compliance with organizational security and governance requirements. Prepare technical documentation, deployment guides, release notes, and operational runbooks. 5. Collaboration & Continuous Improvement Collaborate closely with business stakeholders to understand requirements and business processes. Partner with UX/UI designers to deliver intuitive AI-powered user experiences. Work alongside Dev Ops, Cloud, Platform Engineering, and Security teams to deploy and maintain AI applications. Share technical expertise, reusable code, and engineering best practices. Support demonstrations, user training, and adoption of AI solutions. Technical Skills

The successful candidate should have experience with:

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

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

Financial Services Banking Telecommunications Enterprise Technology Cloud Platforms Knowledge Management Workflow Automation Test Automation Conversational AI Voice AI Responsible AI Secure-by-Design Engineering Core Competencies Full Stack Software Development AI Application Development Problem Solving & Analytical Thinking Innovation & Creativity Communication & Stakeholder Management Team Collaboration Agile Delivery Continuous Learning Adaptability Attention to Detail Customer Focus Results Orientation Key Performance Indicators (KPIs)

Performance will be measured against:

Timely delivery of AI applications and enhancements Software quality, maintainability, and reusability User adoption and satisfaction AI solution accuracy and reliability Successful enterprise system integrations Application performance, scalability, and availability Compliance with security and governance standards Contribution to reusable engineering assets and best practices Career Development

Potential career progression includes:

Senior AI Engineer Lead AI Engineer AI Solution Architect AI Platform Lead Principal AI Engineer AI Engineering Manager Head of AI Engineering