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 DevOps, 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
• TypeScript
• HTML5
• CSS3
• Modern UI component libraries

Backend Technologies

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

API & Integration

• REST APIs
• GraphQL
• Webhooks
• Event-driven architecture
• Enterprise system integration

Databases

• PostgreSQL
• SQL Server
• Cosmos DB
• Relational and NoSQL databases

AI Technologies

• Large Language Models (LLMs)
• OpenAI APIs or equivalent AI platforms
• Retrieval-Augmented Generation (RAG)
• Embeddings
• Vector databases
• AI prompt engineering
• AI evaluation frameworks

AI Frameworks

• LangChain
• LangGraph
• Semantic Kernel
• Similar AI orchestration frameworks

Security

• OAuth
• JWT
• Role-Based Access Control (RBAC)
• Secure API development
• Identity and Access Management

DevOps

• Git
• GitHub
• Azure DevOps
• 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