Horrazon.AI is building a next-generation, AI-powered Marketing Analyst Agent designed to enable businesses to make data-driven marketing decisions autonomously. The platform combines predictive machine learning models for customer lifetime value (CLTV) and return on ad spend (ROAS) with a full-stack SaaS architecture to deliver actionable insights and automated campaign optimization. Horrazon.AI integrates inference services and application layers to move beyond prototype analytics into production-grade, scalable deployments. This description outlines the technology stack, the full-stack internship role, the responsibilities and skills required, the application pathway, and commonly asked questions for candidates interested in contributing to this AI-driven marketing platform.
Core proposition
Horrazon.AI is building a next-generation AI-powered Marketing Analyst Agent to help businesses make data-driven marketing decisions autonomously.
About Horrazon.AI and its mission
Horrazon.AI develops an AI-first SaaS product focused on marketing analytics and automated campaign optimization. The platform is centered on predictive ML models such as CLTV and ROAS and couples those models with an engineering stack designed for production deployment. The objective is to provide businesses with actionable insights that drive marketing decisions, while automating routine optimizations to improve campaign outcomes.
What the product aims to deliver
- Actionable analytics derived from predictive CLTV and ROAS models.
- Automated campaign optimization driven by model inference and business rules.
- An integrated full-stack SaaS architecture that supports analytics, dashboards, and conversational UIs.
Horrazon.AI emphasizes integration between machine learning inference and application-level workflows so that insights can be immediately operationalized. The platform targets moving a production-grade AI system from prototype stage to scalable deployment, reflecting an emphasis on both accuracy and operational reliability.
Target users and impact
- Marketing teams seeking predictive analytics for spend efficiency.
- Product teams wanting integrated ML-driven metrics like CLTV and ROAS.
- Engineering teams focused on deploying and maintaining scalable AI services.
Technology Stack and architecture
The technology stack combines modern frontend frameworks, a performant backend language, and an ML layer built with widely used libraries and models. The frontend is built with React.js, enabling responsive dashboards and component-driven interfaces. The backend is implemented in Go (Golang) to serve RESTful APIs and handle concurrency and performance requirements. The ML layer is implemented in Python and leverages XGBoost and deep neural network models for predictive tasks.
Stack components
- Frontend: React.js.
- Backend: Go (Golang).
- ML Layer: Python with XGBoost and DNN models.
- Planned cloud deployment: AWS services such as S3, SageMaker, RDS, and related infrastructure.
The architecture supports integration across these layers for real-time and near-real-time marketing analytics. The integration points include REST and gRPC patterns to connect frontend components, backend services, and ML inference endpoints. Data pipelines are expected to bring marketing event data through preprocessing and inference stages into dashboards and optimization workflows.
Infrastructure and deployment focus
- Planned AWS deployment that can include object storage, managed inference, and relational databases.
- Production-grade concerns such as scalability, reliability, and secure data access.
- Pipeline work that supports model inference, metrics calculation, and automated campaign actions.
Read More: Free Web Design Tutorial
Role overview: Full-Stack Developer Intern
The Full-Stack Developer Intern role is intended for engineers who will build and integrate core components of Horrazon.AI's AI-driven SaaS platform. Interns work across frontend, backend, and integration layers and contribute to the transition from prototype systems to scalable production deployments. The position includes hands-on involvement with system architecture, API design, and connecting ML services to application workflows.
Primary responsibilities
- Develop and optimize responsive user interfaces in React.js for dashboards, analytics, and conversational UI.
- Design and implement RESTful APIs in Go, handling authentication and multi-tenant access control.
- Integrate frontend, backend, and ML services using REST and gRPC; assist in building data pipelines for real-time analytics.
Interns help implement JWT-based authentication, enforce multi-tenant data architecture and access control, and optimize backend components for concurrency and overall performance. The role expects collaboration with ML engineers to support CLTV, predictive ROAS, and CAC analysis model integration and data flow between inference services and the application layer.
Learning and exposure
- Exposure to AWS architecture and deployment pipelines as part of moving services toward scalable deployment.
- Experience in integrating ML inference services into full-stack applications.
- Opportunities to shape system integration patterns and production-readiness processes.
Read More: Free ChatGPT Tutorial
Key responsibilities and daily tasks
Day-to-day work as a Full-Stack Developer Intern includes frontend development, backend engineering, system integration, and collaborating on AI/ML interaction pipelines. On the frontend, tasks focus on building reusable components, creating responsive dashboards, and improving performance through techniques like lazy loading, code splitting, and effective state management. On the backend, tasks include designing RESTful APIs, implementing JWT-based authentication, and ensuring multi-tenant data isolation and access control.
System integration and ML interaction
- Integrate React frontend, Go backend, and Python ML services using REST and gRPC where appropriate.
- Assist in building data pipelines that move event and customer data through preprocessing to model inference.
- Support integration of CLTV, predictive ROAS, and CAC analysis models and manage the data flow between inference services and the application layer.
Interns will also work on optimizing the backend for concurrency and performance and will participate in infrastructure tasks related to cloud deployment and scalable backend setup. The role demands collaboration across product, engineering, and ML teams to ensure analytical outputs are actionable within the SaaS product. This position emphasizes practical engineering work with a clear operational focus on taking models and services into production.
Team and process expectations
- Work closely with product and ML teams to ensure model outputs map to business-facing metrics and actions.
- Participate in code reviews, develop testable components, and contribute to deployment pipelines and monitoring strategies.
- Help maintain secure client-server interactions and adherence to web security concepts.
Required and preferred skills
Candidates should demonstrate strong proficiency in TypeScript and React.js/Next.js and possess a solid backend understanding in Go. Knowledge of REST APIs, gRPC, and client-server architecture is required, as is familiarity with SQL and NoSQL databases. Foundational knowledge in data structures and algorithms, version control with Git, and web security concepts is expected.
Required technical competencies
- Strong proficiency in TypeScript and React.js/Next.js.
- Solid backend understanding in Go (Golang).
- Knowledge of REST APIs, gRPC, and client-server architecture.
- Familiarity with SQL/NoSQL databases.
- Fundamentals of data structures & algorithms, Git, and web security concepts.
Preferred qualifications include experience with Golang, exposure to ML concepts or data pipelines, familiarity with AWS or other cloud platforms, and understanding SaaS architectures and multi-tenancy patterns. Startup or product experience is listed as an advantage, reflecting the fast-paced, cross-functional nature of the work.
Professional and learning attributes
- Strong problem-solving mindset and ability to debug cross-layer issues.
- Interest in working on systems that combine ML inference and application logic.
- Desire to contribute to production-grade, scalable deployments from prototype systems.
Read More: Tata Free Data Analytics Virtual Experience Program 2026
Application process, eligibility, and selection
Eligibility requires current enrollment in or recent completion of a degree in computer science, information technology, AI, data science, or related fields. Applicants should demonstrate a strong problem-solving mindset and be prepared to show practical work through code repositories and project artifacts. The application is submitted via Unstop and must include an updated resume as well as mandatory GitHub or portfolio links with brief project descriptions.
How to apply
- Submit an application on Unstop with an updated resume.
- Include GitHub or portfolio links; these links are mandatory.
- Provide brief descriptions of relevant projects to showcase practical experience.
The selection process consists of an initial resume screening, followed by a technical interview, and concluding with a final discussion. The technical interview assesses proficiency across frontend, backend, and integration topics, as well as familiarity with ML interaction patterns and cloud deployment concepts. Successful candidates are expected to demonstrate both the technical skills and the collaborative mindset required to help move AI systems into production.
Post-selection expectations
- Engage in hands-on implementation and integration work across the stack.
- Collaborate with ML and product teams to align engineering efforts with business needs.
- Participate in deployment and scaling activities that prepare services for production use.
Read More: Google Paid Internships & Apprenticeships 2026
Frequently Asked Questions
What is Horrazon.AI building?
Horrazon.AI is building a next-generation AI-powered Marketing Analyst Agent that helps businesses make data-driven marketing decisions autonomously. The platform integrates predictive ML models like CLTV and ROAS within a full-stack SaaS architecture to offer actionable insights and automated campaign optimization.
What technologies are used in the stack?
The stack includes React.js for the frontend, Go (Golang) for the backend, and Python for the ML layer using XGBoost and deep neural network models. Planned cloud deployment uses AWS components such as S3, SageMaker, and RDS.
What does the Full-Stack Developer Intern do?
Interns build and integrate core components across frontend, backend, and system integration layers. Responsibilities include developing React UIs, designing RESTful APIs in Go, integrating ML services, and assisting with data pipelines and cloud deployment tasks to move systems toward production readiness.
What skills are required to apply?
Required skills include strong proficiency in TypeScript and React.js/Next.js, solid backend understanding in Go, knowledge of REST APIs and gRPC, familiarity with SQL/NoSQL, fundamentals of data structures and algorithms, Git, and web security concepts. A problem-solving mindset is essential.
How do I apply and what is the selection process?
Apply via Unstop with an updated resume, mandatory GitHub or portfolio links, and brief project descriptions. The selection process involves resume screening, a technical interview, and a final discussion to assess technical fit and readiness for production-focused work.
Conclusion
Horrazon.AI seeks Full-Stack Developer Interns to contribute to an AI-driven marketing analytics platform that combines predictive CLTV and ROAS models with full-stack engineering for actionable automation. The role offers practical exposure to React.js frontends, Go backends, and Python ML models within a planned AWS deployment environment. Successful candidates bring TypeScript and React skills, backend understanding in Go, familiarity with APIs and data stores, and a problem-solving mindset to help move prototype systems to scalable production. Applications are submitted via Unstop with mandatory GitHub or portfolio links and a concise project summary.
