About

Learn more about me

Software Developer

Passionate Software Engineer with expertise in developing and deploying advanced machine learning models.

  • Birthday: 3 May 2001
  • Phone: +571 245 7890
  • City: Washington DC, USA
  • Age: 24
  • Degree: Master of Science
  • Email: omkarbalasaheb.mane@gwu.edu

With a solid foundation in computer science and hands-on experience in machine learning, I am dedicated to creating innovative solutions that drive business success. My background includes developing sentiment analysis models, leading real-time gameplay assistance projects, and enhancing voice assistant systems. I thrive in fast-paced environments and am always ready to tackle new challenges. My proficiency in Python, TensorFlow, and data analysis enables me to transform complex data into actionable insights. Let's connect to explore how we can drive technological advancements together.

Skills

Python 100%
Node.JS 90%
React.JS 92%
Docker 85%
SQL 90%
AWS 95%

Interests

Cooking

Tech Blogs

Fitness

Volunteering

Resume

Check My Resume

Sumary

Omkar Mane

Innovative and detail-oriented Machine Learning Specialist with hands-on experience in developing and deploying advanced machine learning models. Skilled in enhancing data quality and creating efficient solutions for sentiment analysis and voice assistance. Adept at collaborating with cross-functional teams to deliver impactful AI solutions from concept to implementation.

  • 2020 F St NW, Washington, DC
  • (571) 245 3562
  • omkarbalasaheb.mane@gwu.edu

Education

Master of Science in Computer Science

2023 - 2025

The George Washington University, Washington, DC

Coursework includes Machine Learning, Large Language Models, Neural Networks, and Deep Learning.

Software Developer

DEC 2021 - APR 2023

Cognizant, Pune, IND

  • Designed and developed scalable full-stack web applications using React/Next.js, Node.js, and MongoDB, delivering production-grade features with real-time data visualization for financial clients.
  • Deployed RESTful backend services on AWS EC2 and Lambda, integrating CI/CD pipelines with Git, Jenkins, and Docker for automated testing and zero-downtime rollouts.
  • Engineered a dynamic data quality dashboard leveraging React and Redux, incorporating filters, charts, and validation status tracking — decreasing manual QA review effort by 40%.
  • Developed ETL pipelines with Python and Pandas to process millions of financial records daily and stream data via API gateways for downstream applications.
  • Integrated cloud-native services including AWS S3 for secure data storage and CloudWatch for performance monitoring, improving system observability and reducing incident response time by 35%.
  • Refactored backend services into reusable Express.js microservices with token-based JWT authentication, enhancing overall security and maintainability.
  • Collaborated with DevOps and QA teams in an Agile environment, facilitating sprint planning, code reviews, and release coordination across geographically distributed teams.
    • li>Authored detailed architecture diagrams, API documentation, and deployment guides using Confluence, which streamlined team handoffs and accelerated onboarding of new engineers.

Projects

My Projects

GitMatched – Full-Stack Dev Matchmaking Platform

GitMatched – Developer Collaboration Platform: A Tinder-style matchmaking app that connected over 200 developers for hackathons and open-source projects by pairing them intelligently based on skills, tech stack, and goals. I built modular Express.js microservices with stateless JWT authentication and cookie session management, deployed on AWS EC2 with CI/CD pipelines using GitHub Actions. The platform integrated GitHub OAuth 2.0 to auto-generate user profiles and fetch developer metadata, while custom MongoDB match queries using $in, $or, and regex filters improved profile completion rates by 60%. On the frontend, I engineered a responsive swipe-based React UI with RESTful APIs supporting pagination and match request tracking, which increased engagement session duration by 45%.

Serverless Video Processing Service on GCP

Serverless Video Processing Platform: Built a fully serverless platform with a Next.js frontend and an Express.js backend deployed on Google Cloud Run, capable of processing over 1,200 videos with autoscaling and cold starts under 300ms. Integrated ffmpeg to enable real-time format conversion, frame slicing, and metadata extraction, reducing average processing time per video to under 5 seconds. Leveraged Firebase Cloud Storage with signed URLs and lifecycle rules for secure uploads and automatic cleanup, cutting storage overhead by 35%. Automated the entire install and deployment workflow with Docker and GitHub Actions, establishing CI/CD pipelines that reduced deployment time by 75% and eliminated manual errors.

Lip Reading with Deep Learning

This project focuses on developing a deep learning model capable of reading lips using Python and TensorFlow. The model leverages a CNN-RNN architecture to process video frames of lip movements and accurately predict spoken words. By training on large datasets of spoken sentences, the system aims to enhance the accuracy and reliability of lip-reading technology, offering potential applications in communication aids and security systems.

Contact

Contact Me

My Address

2020 F St NW, Washington, DC

Social Profiles

Email Me

omkarbalasaheb.mane@gwu.edu

Call Me

+1 (571) 245 3562

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