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Swipe Right for Your Career: Build A Tinder for Jobs
Latest   Machine Learning

Swipe Right for Your Career: Build A Tinder for Jobs

Last Updated on August 26, 2023 by Editorial Team

Author(s): Anand Taralika

Originally published on Towards AI.

A tech deep-dive to build the ultimate hiring platform using large language models & vector databases

Photo by Cytonn Photography on Unsplash

“Do you know what’s harder than finding a needle in a haystack? Finding a perfect job or candidate in the vast realm of employment! Fear not, dear reader, for we’re about to embark on a quest to build the most impressive job-candidate matchmaking platform. Get ready to dive into the depths of machine learning, LLMs, and vector databases, as we craft a digital Cupid for the job market!”

Ah, the eternal dance between job seekers and employers, akin to an intricate waltz of digital compatibility! In this era of technological marvels, where even toasters can talk to fridges, it’s time to employ the very best of AI to bring forth harmony in the workplace. Our saga unfolds with a carefully architected ensemble of tools and technologies that includes AWS, Hugging Face’s Transformers, and a dash of OpenAI’s GPT. Let’s get technical!

Architecture Overview

The platform will have three main components:

1. Data Ingestion and Storage

  • Resumes and job descriptions are collected from users and employers, respectively.
  • They are preprocessed to clean and tokenize the text.
  • AWS S3 is used to store and manage the data.

2. NLP and Matching Engine

  • Resumes and job descriptions are encoded into dense vector representations using a language model such as GPT or a custom fine-tuned model.
  • Similarity metrics (e.g., cosine similarity) are used to compare vectors and calculate match scores.
  • A threshold is set to filter out low-scoring matches.

3. User Interface and Interaction

  • A web-based user interface allows candidates to upload their resumes and employers to post job descriptions.
  • AWS Lambda and API Gateway handle user interactions.

Sequence Diagrams

1. Candidate Interaction

Candidate -> Platform: Upload Resume
Platform -> NLP Engine: Encode Resume
NLP Engine -> Platform: Matching Scores
Platform -> Candidate: Display Matching Jobs

2. Employer Interaction

Employer -> Platform: Post Job Description
Platform -> NLP Engine: Encode Job Description
NLP Engine -> Platform: Matching Scores
Platform -> Employer: Display Matching Candidates

Code Samples

1. Data Ingestion and Storage: A Symphony in S3 Harmony

We begin our masterpiece by curating the raw materials — the resumes and job descriptions. With the elegance of a conductor leading an orchestra, we utilize AWS S3 to store this treasure trove of textual data. The code orchestrates the upload and download processes, ensuring a seamless flow of information from users to the platform and vice versa.

import boto3

s3 = boto3.client('s3', region_name='your-region', aws_access_key_id='your-access-key', aws_secret_access_key='your-secret-key')

def upload_to_s3(data, filename, bucket_name):
s3.upload_fileobj(data, bucket_name, filename)

def download_from_s3(filename, bucket_name):
obj = s3.get_object(Bucket=bucket_name, Key=filename)
return obj['Body'].read().decode('utf-8')

2. NLP and Matching Engine: The AI Ballet

Ah, NLP, the crown jewel of our ensemble! Picture a prima ballerina on a cosmic stage. Here, we enlist Hugging Face’s Transformers library to turn mere text into waltzing vectors. The code elegantly orchestrates this transformation, crafting a melody of encodings that resonates with the very essence of resumes and job descriptions. And to measure compatibility? Cosine similarity sweeps in, casting a spotlight on the most harmonious pairings.

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def encode_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
return outputs.pooler_output.detach().numpy()

def calculate_similarity(vector1, vector2):
return cosine_similarity([vector1], [vector2])[0][0]

You may choose to use any model of your choice instead of distilbert-base-uncased, e.g., sentence-transformers but be mindful of limitations such as any text more than 384 words gets truncated by default. If you have enough training data, you may also use models such as Word2Vec. However, the code examples and architecture in this article assumes there is no training data available.

3. UI and Interaction: The Technological Tango

Our grand stage wouldn’t be complete without a dance floor for candidates and employers! The Flask framework orchestrates this grand affair. As candidates upload their resumes and employers set the scene with job descriptions in a React.js app, the AI spirits behind the scenes whirl in a data-driven waltz. Results are unveiled as a tapestry of potential matches, a visual testament to the power of technology.

from flask import Flask, request, render_template

app = Flask(__name__)

@app.route("/", methods=["GET", "POST"])
def index():
if request.method == "POST":
text = request.form["text"]
vector = encode_text(text)
# The magical match-making with Pinecone or other vector database
# Display the enchanting results
return render_template("results.html", matches=matches)
return render_template("index.html")

if __name__ == "__main__":
app.run(debug=True)

Please note that the above example is a simplified version of the platform and does not cover all the complexities involved, such as user authentication, error handling, and production deployment. Also, the use of Pinecone or any other vector database would require a separate integration effort that goes beyond the scope of this example.

For a production-level platform, you would need to consider aspects like data security, scaling, model retraining, bias mitigation, and continuous improvement based on user feedback. You might also need to use more advanced NLP techniques and potentially use larger language models like GPT-4 for better understanding and matching of job descriptions and resumes.

Epilogue: Embrace the Future

And there we stand, at the crossroads of technology and humanity, watching the magic unfold. As we bid adieu to our journey, remember this — AI is not just a tool; it’s the masterstroke that paints the canvas of innovation. Our platform doesn’t just match job seekers and employers; it’s a testament to the brilliance that stems from the synergy of minds and machines.

“Dear reader, the quest does not end here. The world of AI and data is a kaleidoscope of endless possibilities. As we dance on the precipice of innovation, I invite you to join me in the waltz of technology as we unravel the tapestry of our digital future. Clap U+1F44F, subscribe U+1F514 , and stay tuned U+1F4E1 for more, for together, we shall continue to paint the future in bytes and brilliance.”

Anand Taralika is a Software Engineer who writes about tech life and the use of tech, data, and machine learning for cybersecurity, finance, healthcare, and sustainable energy. Get stories directly in your inbox so you never miss them!

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