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.

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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.

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Technical Skills

The ideal candidate should possess experience with:

Frontend Development

• React
• Next.js
• TypeScript
• HTML5
• CSS3
• Modern UI component libraries

Backend Development

• Python
• FastAPI
• Node.js
• NestJS
• Express.js

API & Integration

• REST APIs
• GraphQL
• Webhooks
• Event-driven architecture

Databases

• PostgreSQL
• SQL Server
• Cosmos DB
• Other relational or NoSQL databases

AI Technologies

• OpenAI 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

• LangChain
• LangGraph
• Semantic Kernel
• Similar orchestration frameworks

Security

• OAuth
• JWT
• Role-Based Access Control (RBAC)
• Secure API design
• Enterprise authentication

DevOps

• Git
• GitHub
• Azure DevOps
• CI/CD pipelines
• Automated testing

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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).

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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

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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

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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