HopeHire

Landing Page
Onboarding of New User
Setting the Employee medical availability Schedule
Employee View, Choosing Between Recommended Jobs
Employer View, Seeing the Listings He Created

Introduction

Introducing "HopeHire" - an online job search portal that uses the power of machine learning to help kidney patients find employment opportunities.Often overlooked in the job market due to the challenges and time constraints associated with their treatment, kidney patients face unique hurdles in their job search.

That's where HopeHire comes in. By leveraging the capabilities of TensorFlow Recommenders, HopeHire dynamically recommends patients to potential employers and vice versa, taking into account specific attributes that only kidney patients possess, such as proximity to dialysis centers, medical schedule, and the amount of physical labor required at work.

With HopeHire, kidney patients can find meaningful employment opportunities that suit their individual needs, empowering them to achieve their professional aspirations while managing their medical needs.

What it does

HopeHire is a unique job portal that has been tailor-made for individuals with kidney conditions, keeping their health and physical requirements in mind. Using advanced recommendation AI, HopeHire suggests job opportunities that align with their skill set, experience, and most importantly, their health needs.

How we built it


Frontend	React, Recoil, TailwindCSS
Backend	ExpressJS, PostgresSQL
AI	Tensorflow, Flask (for serving)

Challenges we ran into

Although this project was a thrilling and enjoyable experience, we encountered several challenges along the way. These include:

The initial unfamiliarity with TensorFlow recommenders.
The significant amount of time required to integrate the backend with the frontend.
The task of designing an intuitive and meaningful interface that is suitable for kidney patients.

Accomplishments that we're proud of

We are pleased to have accomplished the majority of the product deliverables that we set out to achieve. Our team has successfully integrated the backend services with the frontend and developed a trainable AI system for recommendations.

What we learned

During the development process, we gained insight into the intricacies and significance of designing products that cater to a specific target audience. Creating a purposeful and relevant product requires an understanding of the user's pain points, their preferred mode of interaction with the product, and the design of the product to address these user requirements.

What's next for HopeHire

The following tasks are on our agenda:

Addressing any bugs that surfaced during the integration of the backend with the frontend.
Implementing NLP technology to automatically extract relevant information such as skills and experiences from resumes.
Continuously training and deploying the recommendation AI to enhance its effectiveness.