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 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.
Data Infrastructure And Analytics Technology Information And Internet And Software Development
What We Offer
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