Riipen | ATS software development project

ATS software development project

Closed

Main contact

tCognition Inc

Boston, Massachusetts, United States

Janhavi Bodkhe

Employer

Project

560 hours per participant

Participant

Anywhere

Advanced level

Project scope

Skills

typescript recommender systems jest (javascript testing framework) applicant tracking systems microservices docker compose agile methodology apache kafka postgresql project management

Details

The project involves the development and refinement of various modules within XSEED.ai, a next-generation Applicant Tracking System (ATS) by tCognition Inc. The goal is to enhance the recruitment process by integrating advanced features such as video resumes, robust analytics, and a powerful candidate recommendation system. Students will work closely with tCognition's experienced engineers and project managers to apply their classroom knowledge to real-world scenarios. The project aims to create an efficient, data-driven platform that matches top talent with the right opportunities, ultimately shaping the future of recruitment technology. Key focus areas include user interface design, backend development, and data analytics.

Deliverables

Modules to be Developed:

Dashboard
Job Requisition
Job Setup
Candidate Sourcing
Candidate Management
Candidate Interviews
Offer Management
Reports
Role Management
Analytics
Recommendation System

Technology Stack:

Frontend: React with TypeScript, Material UI for CSS, Jest with React Testing Library for Unit Testing, Mirage JS for API mocking, Formik for internal state management, and Yup for validations.
Backend: Java (Microservices), PostgreSQL, MongoDB.
Cloud Provisioning: AWS, Terraform.
Middleware: Kafka for messaging, Kafka Connect, Kafka Streams.
Deployment: Docker, Docker-compose.

Project Execution Methodology:

Agile Development: The project will follow an Agile methodology with 14 sprints over 28 weeks, each sprint lasting 2 weeks.
Sprint 0: A four-week preparation sprint to establish the development environment and tools.
Application Demo: At the end of every sprint (every 2 weeks), students will present a demo showcasing their progress.

Mentorship

Support and Resources: Students will be paired with experienced Data Scientists, AI/ML Engineers, and Project Managers to guide them through the development process. They will also have access to necessary tools, repositories, and documentation. Additionally, students can include the demos created during this project in their portfolios.