SmartHire
Automated Resume Screening System
Requested By:
Niagara College Toronto
Community Sponsored Project
Date:
Winter 2024, Week 4 Submission
Team Members:
Biswas Rana
Harshkumar Pravinkumar Patel
Hoishal Tamang Lo
Nirmal Shahi
Raj Kumar Budha Magar
Sagar Belbase
Sameer Adhikari
Letter of Transmittal
To: Dr. Manisha Krishnan
From: Biswas Rana
Harshkumar Pravinkumar Patel
Hoishal Tamang
Nirmal Shahi
Raj Kumar Budha Magar
Sagar Belbase
Sameer Adhikari
Date: June 15, 2025
Subject: Submission of Project Proposal (Prototype 2) for PROG1440: Community Sponsored
Project
Dear Dr. Krishnan,
Please accept this document as the formal submission of our Project Proposal for the
second prototype phase of the Community Sponsored Project (PROG1440). This
proposal outlines our project, Smart Hire, an intelligent recruitment platform
designed to connect job seekers with employers and streamline the hiring process.
This document has been prepared in accordance with the instructional guidelines and
grading rubric provided. It details the project's background, objectives, functional
specifications, technical design, and progress to date. We have incorporated the
feedback from our initial prototype review, focusing on enhancing mobile accessibility
and streamlining the user interface for both job seekers and employers. Drawing
inspiration from our initial prototype concept, this version provides a more detailed
breakdown of the AI-driven functionalities, risk management strategies, and market
positioning.
We have conducted a thorough analysis of the problem domain, defined the project
scope, and developed a detailed plan for the creation and management of the
platform's core functionalities and database. Our progress is aligned with the project
management lifecycle, and we are on track to meet our objectives for the upcoming
project phases.
We are confident that Smart Hire offers a viable and valuable solution to the
challenges of modern recruitment. We look forward to discussing this proposal with
you further.
Sincerely,
Biswas Rana
Harshkumar Pravinkumar Patel
Hoishal Tamang Lo
Nirmal Shahi
Raj Kumar Budha Magar
Sagar Belbase
Sameer Adhikari
Smart Hire: An Intelligent Recruitment Platform
Project Proposal: Prototype 2
Course: PROG1440 - Community Sponsored Project
Submitted to:
Dr. Manisha Krishnan
Niagara College - Toronto School of Management
Submitted by:
Biswas Rana
Harshkumar Pravinkumar Patel
Hoishal Tamang Lo
Nirmal Shahi
Raj Kumar Budha Magar
Sagar Belbase
Sameer Adhikari
Date of Submission: June 15, 2025
Executive Summary
HR professionals are under a serious bottleneck of traditional recruitment process.
Not only is manual resume screening inefficient and time-consuming, but it also rests
on human bias, therefore resulting in missing out on qualified people and the absence
of diversity in hiring.
This initiative describes the creation of a centralized intelligent hiring platform called
Smart Hire to fit in the current job market. Smart Hire will take advantage of Artificial
Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) to
digitize the resume screening method moving the entire procedure through
automation and efficiency. The system will then be able to compare resumes to job
descriptions, sort candidates according to objective, data-justified measures and
provide a recruiter with a prioritized list, the saving of which is dramatic in comparison
and enhancement of the quality of hiring decisions.
It will have the capability to include AI-enabled resume parsing and job matching, a
detailed candidates and employers’ profile, simplified application tracking system
(ATS), and interview scheduling functions. The front-end is going to be created to
have easy and intuitive user experience, and the back end will be generated on the
foundation of a scalable architecture using MERN (MongoDB, Express.js, React,
Node.js) stack, with Python serving the functions of a machine learning.
The document contains a fully developed functional specification with a market
analysis of the existing recruitment solutions, fully developed functional and nonfunctional requirements, a proposed schema of a database, and low-fidelity mockups of design. It also explains the process of the project, how the risk will be handled
and a clear roadmap of how a well-built prototype will be delivered. The project is
viable, technically acceptable and serves an apparent need of a more efficient and
equal recruitment process.
Table of Contents
1. Introduction/Background
2.
3.
4.
5.
6.
1.1. Purpose of the Project
1.2. Goals and Objectives
1.3. Feedback from Previous Review
Content
2.1. System Functionality
2.2. Front-End Design and Content
Technical Correctness
3.1. Feasibility Study
3.2. Functional and Non-Functional Requirements
3.3. Market Analysis
3.4. Tools and Justification
Process Details
4.1. Problem Analysis
4.2. Justification for Functionalities
4.3. Database Creation and Handling
4.4. Risk Management
Progress
5.1. Project Management
Conclusion
References
7.
8. Glossary
List of Figures
●
Figure 1: Login page
Figure 2: Signup page
Figure 3: Job posting page
Figure 4: Created job page
Figure 5: Application track page
●
Figure 5: Candidate page
●
Figure 5: ERD
●
●
●
●
List of Tables
Table 1: Functional Requirements
● Table 2: Non-Functional Requirements
● Table 3: Market Competitor Analysis
● Table 4: Risk Management Matrix
●
1.Introduction/Background
Smart Hire is a technologically advanced system that automates the first steps of hiring
by utilizing machine learning (ML) and natural language processing (NLP). It creates a
sorted list of the best candidates by analyzing resumes based on job descriptions and
analyzing candidate profiles according to qualifications, experience, and skills. This
simplification improves standardization and accuracy in candidate evaluation while also
speeding up the recruiting process.
1.1. Purpose of the Project
The primary purpose of the Smart Hire project is to create a dedicated career platform
that streamlines the recruitment process for job seekers and employers. Traditional
hiring methods are struggling to keep pace with the digital age, forcing recruiters to
spend countless hours manually sifting through hundreds of resumes. This project aims
to replace these inefficient processes with an intelligent, automated system that
facilitates meaningful connections between talent and industry, thereby enhancing
candidates' career prospects and providing employers with a direct pipeline to qualified
talent.
1.2. Goals and Objectives
The overarching goal is to revolutionize the recruitment process by developing a
functional prototype that demonstrates the value of AI in modern talent acquisition.
Project Objectives:
●
●
●
●
●
To Reduce Screening Time: Decrease the manual time spent on resume
evaluation by at least 70%.
To Minimize Bias, provide an objective, data-driven ranking to ensure fair and
consistent evaluation of all candidates, mitigating unconscious human bias.
To Improve Quality of Hire, Increase the accuracy of candidate-job matching to
present recruiters with a higher concentration of qualified applicants.
To enable employers to post job openings and manage applications efficiently
through an integrated system.
To develop a platform for job seekers to build professional profiles and discover
relevant opportunities.
1.3. Feedback from Previous Review
Comprehensive feedback on our initial prototype highlighted several areas for
improvement, which this Prototype 2 proposal directly addresses. The feedback has
been instrumental in enhancing the project's depth, clarity, and overall structure. Key
improvements include:
●
●
●
●
●
Structural and Rhetorical Enhancements: The document’s organization and
formatting have been reworked for better logical flow and professionalism. All
required sections, including a detailed introduction, clearly defined objectives, a
robust market research section, and a formal conclusion, have been fully
developed to create a more persuasive and complete proposal.
Expanded Scope and Risk Management: The project scope has been more
clearly defined, and the section on risks and benefits has been expanded to
include a detailed risk management matrix, addressing potential issues like
algorithmic bias and data privacy proactively.
Detailed Technical Specifications: The Software Requirement Specification
(SRS) has been expanded beyond a basic list to include more detailed functional
and non-functional requirements and a Level-0 Data Flow Diagram (DFD) to
visualize the system’s core process. A specific discussion on the mapping of
candidates to jobs is now integrated into the system functionality.
Comprehensive Project Plan: The project plan is no longer partial and now
includes references to a detailed Work Breakdown Structure (WBS), PERT chart
analysis for managing dependencies, and a clearer project timeline.
Mobile-First Design Philosophy: In addition to the structural feedback, this
prototype phase has adopted a mobile-first design philosophy. All wireframes
and design considerations prioritize the mobile view to ensure usability on the
devices users frequent most. The authentication and registration flow has also
been redesigned to be more intuitive for both job seeker and employer user
groups.
2. Content
Smart Hire is a dual-sided recruitment platform designed for both job seekers and
employers, offering intelligent, automated hiring tools through a user-friendly
interface. Below is an integrated overview of its core functionalities and how they are
visually and interactively presented on the front end.
1.Internal & External Job Posting
Functionality:
Employers can create, edit, and manage job postings, with visibility settings to make
posts internal (for existing employees) or external (for public applicants).
User Benefit:
HR can control who sees the job post, and candidates have clear visibility of relevant
opportunities.
Front-End Design:
React-based forms for creating job listings, with toggle options (Internal/External),
responsive layout, and card-style listing displays for easy browsing.
2. Automated Resume Screening
Functionality:
Uploaded resumes (.pdf, .docx) are automatically parsed using Natural Language
Processing (NLP) to extract structured data—skills, experience, and education. A
Machine Learning model ranks candidates based on semantic match with job
descriptions.
User Benefit:
Recruiters save time and effort; job seekers benefit from fair and efficient evaluation.
Front-End Design:
A drag-and-drop upload interface with progress indicators; parsed data shown in a
clean, tabular format on the employer side.
3. Candidate Shortlisting
Functionality:
An Application Tracking System (ATS) allows employers to view and shortlist
candidates from a ranked list. Recruiters can manually adjust shortlist based on
profile highlights.
User Benefit:
Recruiters focus only on top-quality candidates, and applicants get timely status
updates.
Front-End Design:
Employer dashboard with candidate cards, match scores, filters (experience, skill
match, education), and status badges (e.g., "Shortlisted", "Interview Scheduled").
4. Rejection Letters
Functionality:
The system provides employers with customizable, professional rejection letter
templates.
User Benefit:
Reduces follow-up from candidates and maintains a positive image of the company.
Front-End Design:
Bulk-action options for sending rejection letters via a simple modal; includes
personalization fields and pre-set templates.
Overall Front-End Experience
•
Technology: Built in React.js with a responsive grid layout, ensuring usability
across devices.
•
Navigation: Role-based interfaces for employers and job seekers, each with
customized menus and dashboards.
•
Visual Style: Clean, minimalistic UI with a focus on clarity, content hierarchy,
and ease of interaction.
•
Content Display:
o
Job listings shown as concise cards with essential info.
o
Candidate rankings and parsed resume data displayed in sortable tables
or cards with visual match indicators.
Figures of prototype
Fig 1.Login page
Fig 2.Signup page
Fig 3.Job posting page
Fig 4.Create job page
Fig 5.Application track page
Fig 6.Candidate page
3. Technical Correctness
3.1. Feasibility Study
Technical Feasibility: The project is technically feasible. The chosen technology
stack is well-suited for this application. The AI matching component will be
implemented using established Python libraries for NLP and ML.
● Economic Feasibility: The project is economically feasible, relying on opensource technologies. Cloud services can be used for cost-effective deployment.
● Operational Feasibility: Once deployed, the platform can be managed with
minimal technical overhead, with automated processes handling most of the core
functionality.
●
3.2. Functional and Non-Functional Requirements
Table 1: Functional Requirements
ID
Requirement
FR1
The system shall allow job seekers and
employers to register for separate account
types.
FR2
The system shall allow users to upload multiple
resumes (in .pdf and .docx formats).
FR3
The system shall automatically parse uploaded
resumes to extract structured information.
FR4
The system shall score and rank candidates
based on how well they match a job
description.
FR5
The system shall provide employers with a
dashboard to view and manage ranked
applicants.
FR6
The system shall allow users to export the
ranked candidate list as a CSV file.
Table 2: Non-Functional Requirements
ID
Requirement
NFR1
The web application must be responsive and
functional on all major browsers.
NFR2
The system should process and rank a single
resume in under 3 seconds.
NFR3
The ML model should achieve a ranking
accuracy of over 85% in classification tests.
NFR4
All user data must be encrypted both in transit
(TLS) and at rest.
NFR5
The system shall have an uptime of 99.5% and
include error logging.
NFR6
The user interface must be intuitive and require
minimal training for a non-technical HR user.
Market Analysis
The global HR technology market is experiencing explosive growth, driven by the
adoption of AI. While the market has several large players, there is a significant
opportunity for a specialized tool like SmartHire that focuses on accuracy and ease of
use.
During the literature review, we examined recent studies that look at important parts of
AI in recruitment.
These include:
•
NLP is used for profiling candidates by looking at each resume and spotting
important data, such as what skills a person has, their education, and work background.
Techniques like machine learning and data analysis help with job matching by selecting
the top candidates for a certain job. Designing algorithms in hiring that aim to check and
decrease the bias related to gender, race, and culture.
•
Using deep learning on unstructured text in resumes helps improve the accuracy
of understanding resumes that people have written.
As a result of these studies, we are developing our own solution, Smart Hire, which has
special features that no other tools possess. Letting recruiters add their comments on
who to hire, enables the AI to improve and get smarter. Personalized weight system:
With Smart Hire, you can give special attention to extra abilities required for various job
positions. By bringing together these elements, our study tries to produce a resume
screening tool that is flexible, understanding, and fair for companies that need to hire
many people.
Table 3: Market Competitor Analysis
Competitor
Approach & Functionalities
How Smart Hire is Different
LinkedIn Talent Solutions
Leverages its vast network for
sourcing and uses AI to
recommend candidates.
SmartHire is a tool for
processing incoming
applications with deeper,
customizable semantic
parsing, whereas LinkedIn is
primarily for sourcing.
HireVue
Enterprise-level platform
SmartHire is focused purely
using AI for video interviews
and resume screening.
on resume screening, making
it a more accessible and
affordable solution for SMBs.
Zoho Recruit
An affordable ATS with some
rule-based automation
features.
SmartHire's ML-driven
approach allows for more
nuanced and intelligent
matching beyond simple
keywords.
Pymetrics
Uses neuroscience games
and AI to assess candidate
soft skills.
SmartHire focuses on the
hard skills and experience
documented in a resume,
complementing behavioral
assessments.
3.4. Tools and Justification
4.4. Risk Management
A proactive approach to risk management is essential for project success. Key risks
have been identified and mitigation strategies planned.
Table 4: Risk Management Matrix
Risk ID
Risk
Description
Probability
Impact
Mitigation
Strategy
R01
Algorithmic Bias
in ML Model
Medium
High
Train the model
on diverse,
anonymized
datasets.
Conduct regular
audits for
fairness and
implement
features for
transparency in
rankings.
R02
Data Privacy &
Security
Low
High
Adhere to data
privacy
principles (e.g.,
GDPR).
Implement endto-end data
encryption,
secure
authentication,
and role-based
access control.
R03
NLP Parsing
Inaccuracy
Medium
Medium
Utilize state-ofthe-art NLP
libraries. Train
the system on a
wide variety of
resume formats.
Incorporate a
feedback loop
for users to
correct parsing
errors, which
will improve the
model.
R04
Low User
Adoption
Medium
Medium
Prioritize UI/UX
design with
input from
potential users.
Develop
comprehensive
documentation
and training
materials.
4.Progress Details
Complete Analysis of the Problem
A crucial HR job is recruitment, but conventional approaches are sometimes disjointed, laborintensive, and slow. Managing internal promotions, finding external candidates, maintaining
compliance, and effectively communicating with applicants all add complexity to the process.
Recruitment involves many steps:
•
•
•
•
Posting a job
Finding candidates
Reviewing applicants
Rejecting or hiring people
Problem
Internal employees don’t see job openings in time
Posting jobs on many websites takes too long
Data from LinkedIn, job boards, and referrals is
scattered
Shortlisting resumes takes hours
Rejected candidates feel ignored
Impact
Missed opportunities for promotion
Slower hiring process
Messy, duplicated candidate data
Wastes HR time and risks
unfairness
Bad impression of the company
Justification for Each Functionality
1.Internal & External Job Posting
What It Solves:
•
•
Makes sure everyone sees job openings both inside and outside the company.
One system to post jobs instead of logging into many job boards.
Why It Matters:
•
•
Encourages internal promotions and retains good employees.
Saves time posting jobs on LinkedIn, indeed, company website, etc.
How It Works at Each Stage:
Stage
Description
Proof of Concept HR can enter internal job info only, saved in a database
Real-time sync to external job sites, options for internal-only
Production
posting
2. Candidate Sourcing
What It Solves:
• Candidates come from LinkedIn, referrals, websites hard to manage in one place.
Why It Matters:
• Allows the team to track all candidates in one place, even if they came from different
sources.
How It Works at Each Stage:
Stage
Description
Proof of Concept Add candidate info manually (name, email, source)
Production
Auto-import from APIs (LinkedIn, job boards, etc.) into database
3. Shortlisting
What It Solves:
• Hundreds of resumes = hours of reading and scoring.
Why It Matters:
• Automates the first round of selection based on skills, education, and experience.
• Saves recruiters’ time, improves fairness.
How It Works at Each Stage:
Stage
Description
Proof of Concept Simple rule: show candidates with 4+ years experience
Production
AI model ranks top candidates recruiters can review or override manually
4. Letter of Rejection
What It Solves:
• Rejected applicants feel ghosted or receive cold responses.
Why It Matters:
• Shows professionalism, protects your company reputation, and keeps the door open for
future roles.
How It Works at Each Stage:
Stage
Description
Proof of Concept Pre-written message selected from a dropdown
Production
Sends personalized email directly to candidate via SMTP or API
Summary of Project Versions
Feature
Proof of Concept
Final Product (Production)
Job
Posting
Internal only, basic form
Post both internal & external, with job board
sync
Sourcing
Manually enter candidates
API integrations with LinkedIn, referrals, etc.
Shortlisting
Manual filter (e.g., by
experience)
AI + Manual shortlist, scoring candidates
Rejection
Choose message from list
Send custom emails to rejected applicants
DATABASE EXPLANATION
Database Table Relationships Explanation
IN SQL
Jobs -----< Applications >----- Candidates
|
|
PostedBy (User)
Source
|
Rejections
This structure shows how your recruitment system stores and connects data. Let’s go through it
one part at a time:
Figure of Dataflow of Smart Hire(Fig 7)
1. Jobs Table
Each job that’s posted whether internal or external is stored in this table.
Sample Columns:
• JobID (Primary Key)
• Title
• Description
• Type (Internal or External)
• Data created
Purpose: Stores all job postings. Connected to:
1. Applications (who applied to which job)
2. Users (who posted it, e.g., HR manager)
2. Users Table
This includes people using the system (HR Manager, Recruiters).
Sample Columns:
• UserID (Primary Key)
• Name
• Role (HR Manager, Recruiter)
• Email, PasswordHash
Purpose: Tracks who is doing what in the system.
1. Connected to Jobs via Posted By.
3. Candidates Table
Each person who applies to a job is stored here.
Sample Columns:
• CandidateID (Primary Key)
• Name, Email, Phone
• ResumeFilePath
• Source (e.g., LinkedIn, Referral, Indeed)
Purpose: Stores personal details of all applicants.
1. Connected to Applications
2. Connected to Rejections (if not selected)
4. Applications Table
This is the bridge table that connects a candidate to a job—they applied for it.
Sample Columns:
• ApplicationID (Primary Key)
• CandidateID (Foreign Key)
• JobID (Foreign Key)
• AppliedDate
• Status (Applied, Shortlisted, Hired, Rejected)
• Score (optional, for shortlisting AI)
Purpose: Stores every application for every job.
1. Candidate X applies to Job Y → that gets one row in this table.
5. Rejections Table
If a candidate is rejected, this table stores rejection info.
Sample Columns:
• RejectionID (Primary Key)
• CandidateID (Foreign Key)
• JobID (Foreign Key)
• MessageTemplateID (Foreign Key → Templates)
• RejectedBy (UserID)
• RejectionDate
• Stage (Pre-interview, post-interview, etc.)
Purpose: Tracks who was rejected, for what job, when, and with which message.
1. Optional: stores if the rejection was automatic or manual.
2. Useful for audit trails and reporting.
Sources of Ideas Referenced
• Industry Practices
Insights drawn from established recruitment platforms:
o Linkdin
o Indeed,for Employers
•
UI/UX Patterns
Design principles and layout inspirations taken from:
o Figma Community Templates
o Canvas designing
•
Automation & Candidate Experience Strategies
Concepts informed by modern recruitment tech reports and blogs:
o Lever Blog – Recruiting Best Practices
o Recruited Blog – Hiring Automation & Employer Branding
Project Management
The project focuses on developing a Resume Review and Hiring Application, aimed at helping
employers and HR professionals automate the resume screening process during recruitment.
The goal is to reduce the manual workload involved in filtering resumes and identifying the most
qualified candidates for job openings.
This app will use Artificial Intelligence (AI) and Natural Language Processing (NLP) to analyze,
evaluate, and rank resumes based on how well they match specific job descriptions or
requirements. It is developed by our team of 7 members who each contribute based on their
technical and managerial strengths. The team is using Agile methodology, specifically the
Kanban framework in Jira, to manage the project's progress in a flexible and visual way.
Screenshot of Kanban board of our project
To manage our project effectively, we used Kanban in Jira, a visual project management
method that helped us organize and track our work throughout the semester. The Kanban board
was divided into key columns: To Do, In Progress, Review, and Done. Each task—whether it
was frontend development, backend API creation, ML integration, or testing—was added as a
card on the board and moved through the stages as work progressed.
This system allowed all team members to see what tasks were pending, what was actively
being worked on, and what had been completed. It helped us avoid confusion or duplication of
work and ensured smooth collaboration. We also used labels and assignments to specify who
was responsible for each task. By breaking the project into smaller tasks and tracking them
visually, Kanban helped us stay organized, maintain momentum, and complete the Smart Hire.
Process timeline
Calendar of our project planning in Jira
Screenshot of our Jira management timeline
Over the course of the semester, our team of seven developed Smart Hire, an AI-based resume
screening app, by dividing the project into clear phases: planning, design, development,
integration, testing, and finalization. We used Jira with a Kanban board to manage and track
tasks efficiently, allowing each team member to focus on their role frontend, backend, ML,
UI/UX, QA, or documentation.
Early in the semester, we focused on defining the concept, assigning roles, and designing the
system architecture and UI. Development followed, where we built the frontend using React, the
backend with Flask, and implemented a machine learning model to rank resumes based on job
requirements. Integration brought all components together into a working system, followed by
testing and debugging to ensure quality.
By the end of the semester, Smart Hire will fully be ready for presentation, demonstrating
effective teamwork, consistent progress, and successful project management.
Task of each member in Jira project management
Biswas rana
1. Set up ML environment
Install and configure Python, libraries (e.g., NLP, ML frameworks), and tools needed for model
development.
2. Develop NLP model
Code the model to parse resumes and extract skills, experience, and education using NLP
techniques and pre-trained models.
3. Test model accuracy
Evaluate the model using test data to ensure extraction accuracy exceeds 85%, and fine-tune
as needed.
4. Employer Dashboard and UI
Integrated model results into the employer dashboard, enabling display of ranked candidates
with match scores and summaries for easy review.
5. Build NLP Model to Extract Resume Info
Developed an NLP model to automatically extract skills, education, and experience from
uploaded resumes (.pdf/.docx).
Harshkumar Pravinkumar Patel
Frontend Tasks:
1. Develop React components for Registration and Login forms
Create user-friendly forms to handle user signup and authentication.
2. Develop frontend components for uploading .pdf and .docx files
Build UI elements that allow users to upload resumes in PDF and DOCX formats.
3. Develop React components for the results dashboard
Design and implement the dashboard to display parsed resume data and analytics.
4. Add an "Export to CSV" button on the frontend dashboard
Enable users to export dashboard data as CSV files for offline use.
5. Initialize React.js frontend project with component structure
Set up the React project with a clean folder and component hierarchy for scalable
development.
Hoishal Tamang ’s UI/UX Design Tasks:
1. Design UI mockups for Registration and Login pages
Create intuitive and visually appealing mockups for user signup and login screens.
2. Design a clean, data-centric dashboard UI
Develop a dashboard layout focused on displaying ranked candidates, match scores,
and qualification summaries clearly.
3. Design UI for a simple, multi-file upload interface
Create an easy-to-use interface that supports uploading multiple resume files (.pdf,
.docx) simultaneously.
Nirmal Shahi’s Backend & Deployment Tasks:
1. Create backend API endpoints for user registration and login
Build secure APIs to handle user signup and authentication.
2. Implement password hashing and secure session management
Ensure user credentials are safely stored and sessions are managed securely.
3. Create backend endpoint to receive and temporarily store uploaded documents
Develop APIs to accept resume uploads and store them temporarily for processing.
4. Create secure API endpoint to integrate Python ML service with Node.js backend
Enable communication between the backend and the NLP model service securely.
5. Create backend API to send ranked candidate data to frontend
Provide APIs that deliver processed candidate results to the UI.
6. Implement backend logic to generate a CSV file from ranked candidate list
Add functionality to export candidate data as CSV from the backend.
7. Deploy the final prototype to the cloud
Set up deployment pipelines and host the full app on a cloud platform.
8. Set up cloud hosting environments for backend and frontend
Configure cloud servers/environments for both backend services and frontend app.
9. Initialize Node.js/Express.js backend project and connect to MongoDB
Start backend project structure and establish database connection.
Sagar Belbase
o
o
o
o
o
Configure GitHub repository and establish branching strategy
Set up the project repo with clear branching policies for smooth collaboration and
version control.
Prepare the final project report and presentation slides
Compile documentation and create slides summarizing the project for final
submission and presentation.
Core AI/ML Engine
Built the ML model to rank candidates by matching resumes to job descriptions.
Evaluation Script
Created a script to calculate accuracy, precision, and recall to test model
performance.
User Story
As an HR professional, view a ranked candidate list on the dashboard to identify
top talent quickly.
Raj Kumar Budha Magar
•
Collect and preprocess anonymized resume datasets for model training
Gather resume data, remove personal identifiers, and clean the data to prepare it for
training the NLP model.
•
• Assign Team Roles and Responsibilities:
Define who does what in the team to ensure tasks are clear and work is organized.
• Started Skill Extraction Using spaCy and Keyword Matching:
Begin extracting skills from resumes using spaCy (an NLP tool) and matching keywords
to identify relevant skills automatically.
• Initialize Node.js/Express.js Backend Project and Connect to MongoDB:
Set up the backend server using Node.js and Express.js, then connect it to MongoDB to
store and manage data.
•
•
Conclusion
The Smart Hire project exhibits the properly organized and technically feasible solution
to the current ineffective situation in the recruitment environment. The intelligent
automation factor coupled with considerations of user-friendly elements makes the
platform a major advancement in comparison to the typical hiring tools. Its combination
of the solutions based on management of both internal and external postings,
centralization of candidate sourcing, standardization of shortlisting and provision of
professional communication with the applicants improve transparency, efficiency, and
overall user experience on many levels. The level of work done by the team indicates
that it has a considerable potential of producing a high-quality prototype and provides a
good basis of extending it to a more scalable, feature-rich recruitment platform in the
future.
7. References
Atlassian. (n.d.). Jira Software. Retrieved from
https://www.atlassian.com/software/jira
● Jain, R., & Sharma, S. (2021). AI-based intelligent hiring system using NLP. Journal
of Information Technology.
● Kim, J., & Lee, H. (2022). Deep Learning Techniques for Resume Classification.
IEEE Access.
● MongoDB, Inc. (2025). MongoDB Documentation. Retrieved from
●
https://docs.mongodb.com/
● Pressman, R. S., & Maxim, B. R. (2020). Software Engineering: A Practitioner's
Approach. McGraw-Hill Education.
● React. (n.d.). React – A JavaScript library for building user interfaces. Retrieved
from https://reactjs.org/
8. Glossary
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AI (Artificial Intelligence): The simulation of human intelligence in machines
that are programmed to think like humans and mimic their actions.
ATS (Application Tracking System): Software that serves as a central location
for a company’s recruitment efforts.
MERN Stack: A JavaScript-based technology stack consisting of MongoDB,
Express.js, React, and Node.js.
Mobile-First Design: A design strategy that begins with designing for the
smallest screen (mobile) and then scaling up.
NLP (Natural Language Processing): A field of AI that enables computers to
understand, interpret, and manipulate human language.
Resume Parsing: The process of automatically extracting information from a
resume to populate a structured database.
Scalability: The measure of a system's ability to handle a growing amount of
work by adding resources to the system.
UI (User Interface): The point of human-computer interaction in a device.
UX (User Experience): A person's emotions and attitudes about using a
particular product, system, or service.