About VTION.AI

VTION.AI is a data intelligence and technology company focused on leveraging advanced analytics, AI, and technology to generate actionable business insights. We build technology-driven solutions that help businesses understand consumer behaviour, transform data into insights, and make informed decisions.

Role Overview

We are looking for an AI Full-Stack Developer who can build, deploy, and maintain scalable web applications while integrating AI/ML capabilities into real-world products. The ideal candidate should have strong expertise in frontend and backend development, API design, database management, cloud deployment, and hands-on experience working with AI models or LLM-based applications.

Key Responsibilities

Full-Stack Application Development

- Design, develop, and maintain end-to-end web applications using modern frontend and backend technologies.

- Build responsive, intuitive, and high-performance user interfaces.

- Develop scalable backend services, business logic, and RESTful APIs.

- Integrate frontend applications with backend services, databases, and third-party platforms.

- Write clean, reusable, maintainable, and well-documented code.

AI/LLM Integration

- Integrate AI/ML models and Large Language Models (LLMs) into business applications.

- Develop AI-powered features such as intelligent search, automated reporting, data summarisation, conversational interfaces, and insight generation.

- Work with LLM APIs, prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG) where applicable.

- Evaluate AI-generated outputs for accuracy, relevance, latency, and reliability.

- Collaborate with data science and analytics teams to operationalise AI-driven solutions.

Backend, Database & Data Processing

- Design and optimise database schemas, queries, and data access layers.

- Work with relational and/or NoSQL databases such as PostgreSQL, MySQL, MongoDB, or equivalent technologies.

- Develop efficient data processing workflows and integrations to support analytics-driven applications.

- Implement authentication, authorisation, data validation, and secure API integrations.

- Troubleshoot performance bottlenecks and improve application scalability.

Cloud, DevOps & Deployment

- Deploy and maintain applications on cloud platforms such as AWS, Azure, or GCP.

- Work with Docker, CI/CD pipelines, version control, and automated testing.

- Monitor application performance, logs, errors, and system availability.

- Follow secure coding practices and support production releases and incident resolution.

Collaboration & Product Delivery

- Work with Product, Technology, and Data Science teams to translate business requirements into technical solutions.

- Participate in architecture discussions, code reviews, sprint planning, and technical documentation.

- Identify opportunities to improve application performance, development efficiency, and product usability.

- Take ownership of features from development through deployment and ongoing maintenance.

Required Skills & Qualifications

- Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.

- 3 - 6 years of hands-on experience in full-stack software development.

- Strong proficiency in JavaScript/TypeScript and experience with React.js or an equivalent modern frontend framework.

- Backend development experience with Python (FastAPI, Django, or Flask), Node.js, or an equivalent backend technology.

- Experience designing and integrating RESTful APIs and working with databases.

- Practical exposure to AI/ML integrations, LLM APIs, or AI-powered application development.

- Understanding of Git, software testing, debugging, and application security.

- Familiarity with cloud deployment, Docker, and CI/CD workflows.

- Strong problem-solving skills and the ability to work independently in a fast-paced product environment.

Good to Have

- Experience with LangChain, LlamaIndex, vector databases, embeddings, or RAG pipelines.

- Exposure to OpenAI APIs or other commercial/open-source LLMs.

- Experience with analytics platforms, dashboards, data visualisation, or data-intensive applications.

- Familiarity with Redis, asynchronous processing, message queues, or microservices.

- Experience building AI agents, workflow automation, or evaluation pipelines for AI applications.

- Exposure to data privacy, secure data handling, and enterprise-grade application development.

Key Success Metrics

- Timely delivery of high-quality product features.

- Application performance, scalability, reliability, and maintainability.

- Successful integration and deployment of AI-powered capabilities.

- API response times, defect rates, and production issue resolution.

- Code quality, test coverage, and adherence to engineering standards.

- Effective collaboration with Product, Technology, and Data Science teams.