?? Introducing Smart Resume Match: Ace the ATS Game! ??

?? Introducing Smart Resume Match: Ace the ATS Game! ??

Hello LinkedIn Family,

I want to share a journey close to my heart that might resonate with many of you. In the fast-paced world of job hunting, I noticed a recurring issue – countless talented individuals were getting lost in the shuffle, their resumes overlooked by Applicant Tracking Systems (ATS).

Inspired by this challenge, I embarked on a mission to create something that could truly make a difference. Today, I'm thrilled to present Smart Resume Match: Ace the ATS Game!

?? Experience it here: Smart Resume Match

The Backstory

In my career, I’ve seen brilliant minds struggle to get their resumes past automated systems, despite their qualifications and potential. It was disheartening to see friends and colleagues face rejection not because they weren't capable, but because their resumes didn't have the right keywords or format.

The Solution

Harnessing the power of Generative AI, I developed a tool designed to:

? Upload Your Resume: Easily upload your PDF resume.

? Job Description Input: Input the job description for your desired role.

? AI-Powered Analysis: Advanced AI analyzes your resume against the job description.

? Matching Score: Provides a clear matching score with a progress bar and percentage.

? Missing Keywords: Highlights key terms missing from your resume.

? Profile Summary: Generates a comprehensive summary to showcase your strengths.

Technical Journey

Creating this tool was an incredible technical journey. Leveraging Google's Generative AI and Natural Language Processing (NLP), I used Python, Streamlit, and PyPDF2 to build the core functionalities. The AI models were fine-tuned using TensorFlow and PyTorch, and the backend was integrated with Flask. I utilized the Steamlit community cloud for deployment to ensure scalability and reliability. Additionally, I employed LangChain, and Hugging Face Transformers to enhance the AI’s capability in understanding and analyzing the content. This project is a testament to the power of Gen AI and modern tech stacks in solving real-world problems.

Why It Matters

This project isn't just about technology; it's about leveling the playing field. It's about ensuring that your hard work, dedication, and potential aren't missed due to an algorithm. By leveraging Generative AI, we can help more people get the recognition they deserve.

A Call to the Community

Whether you're a job seeker striving for that next opportunity, a recruiter looking for the perfect fit, or a tech enthusiast eager to see AI making a real-world impact, I invite you to explore this tool. Your feedback and support are crucial as we continue to enhance this platform.

Together, let’s make the job search process smarter, fairer, and more inclusive. Let’s ensure that every talented individual gets their chance to shine.

?? Try Smart Resume Match Now: Smart Resume Match

Thank you for being part of this journey. Let's revolutionize the job application process, one resume at a time.

#AI #GenerativeAI #JobSearch #ATS #ResumeTips #CareerDevelopment #TechInnovation #JobHunt #Recruitment #DataScience #MachineLearning #CareerAdvice

Vishnu Sri Ranjan Doddahosahalli Ramesh

Full Stack Developer | Software Developer | Data Science | Data Engineering | Data Analyst | Masters in Information Technology and Analytics at Rutgers University'24

6 个月

Thanks for sharing

Nikhita Kalburgikar

Actively looking Full-Time Opportunities | Data Science - MLOps Intern at GM Financial | Graduate student at UTA | Digital Technology Analyst at NTT DATA Services | Microsoft | AWS Cloud & DevOps, AI, ML, Data Science

6 个月

Great work Mayur Jadhav ????

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Abhijit C

Actively Seeking Full-Time Data Science & Data Engineering Opportunities | MS Data Science | Graduate Research Assistant at UTA Libraries | Machine Learning Enthusiast & Innovator

6 个月

Fantastic work, Mayur Jadhav! This concept has been on my mind as well. I've developed a similar project where you can perform query answering using the RAG architecture for any documents. When I came across your implementation, I knew it was the perfect framework to build upon. I'm definitely going to fork this and give it a try. Additionally, I was wondering if you used Hugging Face models for both embeddings and LLMs and which vector DB you utilized in this? Since OpenAI now requires credits to use its models, I'm curious about your approach. Thanks for the inspiration and for sharing your implementation!

Vaishnavi Sulegai Radheshyam

Data Analyst | SQL, Python, Tableau, Power BI, Machine Learning, AWS | MS in Information Systems | Business Analytics Certified

6 个月

Wow, this is amazing! Thanks for building and sharing it!

Indupriya Bhaskar Basireddy

Data Analyst / Operations Specialist II @ Alcon | Data & Cloud Professional | AWS Certified | Azure | Skilled in Big Data Technologies, ETL Pipelines, Databricks | Proficient in SQL, Python, Bash, Alteryx

6 个月

Good work!

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