About

Learn more about me

Software Developer

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

  • Phone: +571 245 7890
  • City: Washington DC, USA
  • Degree: Master of Science
  • Email: omkarbalasaheb.mane@gwu.edu

With a solid foundation in computer science and hands-on experience in building scalable full-stack and cloud-native applications, I am dedicated to creating innovative solutions that drive business success. My background includes developing high-performance microservices, designing real-time event-driven pipelines with Kafka and Redis, and deploying secure systems on AWS and Kubernetes. I thrive in agile environments and enjoy taking ownership of complex problems end to end. My proficiency in Node.js, TypeScript, Java, Python, and cloud DevOps tools enables me to deliver reliable, production-ready systems. Let’s connect to explore how we can accelerate digital transformation together.

Skills

Python 100%
Typescript 90%
Node.JS 92%
React.JS 94%
Docker 85%
SQL 90%
AWS 95%
Langchain 93%

Interests

Cooking

Tech Blogs

Fitness

Volunteering

Resume

Check My Resume

Sumary

Omkar Mane

Innovative and detail-oriented Full Stack Software Engineer with hands-on experience in designing and deploying scalable microservices, event-driven architectures, and cloud-native applications. Skilled in building high-performance REST and Kafka-based systems, optimizing databases with PostgreSQL and Redis, and deploying secure solutions on AWS and Kubernetes. Experienced in integrating AI/ML workflows into production pipelines, including NLP and document summarization, to enhance automation and efficiency. Adept at collaborating within agile teams to deliver impactful, production-grade solutions with 99.9% uptime.

  • 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

SEP 2024 - Current

MORGAN STANLEY

  • Built and deployed FastAPI + Node.js microservices on AWS Lambda and EC2, handling 2M+ API calls monthly with <200ms latency.
  • Integrated LangChain + Hugging Face Transformers into backend pipelines for document summarization and chatbot workflows, reducing manual query resolution time by 40%.
  • Designed real-time Kafka + Redis streaming pipeline for trade and risk event monitoring, cutting data processing lag from 15s to under 3s.
  • Implemented vector search with Pinecone for retrieval-augmented generation (RAG), enabling faster financial research lookups across 10M+ records.
  • Automated CI/CD using GitHub Actions + Terraform + AWS CodePipeline, shrinking release cycles from bi- weekly to daily.
  • Deployed secure APIs (OAuth2, JWT, rate-limiting) with CloudWatch + X-Ray monitoring, improving system uptime to 99.9% and passing internal penetration tests.

Software Developer

DEC 2021 - July 2023

INFINITE INFOLAB

  • Designed high-throughput API services with FastAPI and Node.js, scaling to 1.5M+ monthly requests while maintaining sub-150ms response times.
  • Implemented Kafka + Redis streaming pipelines to synchronize user activity logs, cutting data propagation delays from 12s to under 2s.
  • Deployed NLP pipelines using SpaCy + Scikit-learn for text classification and keyword extraction, reducing manual document triage effort by 30%.
  • Containerized ML models with Docker and AWS SageMaker, deploying inference endpoints through automated GitHub Actions pipelines.
  • Built React + Next.js dashboards for tracking pipeline health and monitoring model performance, helping product managers identify drift in real time.
  • Introduced Terraform-based infrastructure templates for repeatable cloud deployments, reducing environment setup effort from 3 days to a few hours.

Software Developer

FEB 2020 - NOV 2021

SAGE SOFTTECH

  • Built and deployed Spring Boot + React.js applications serving 50K+ active users, with modular REST APIs and responsive UI for production systems.
  • Optimized PostgreSQL + Redis data layer, redesigning queries and caching to cut API latency by 75% (900ms to 200ms).
  • Designed Kafka-based event streaming pipelines processing 20K+ messages/sec, enabling real-time transaction workflows and analytics.
  • Automated cloud deployments on AWS (EC2, RDS, S3) with Docker, Kubernetes, and Terraform, achieving 99.9% uptime and reducing release cycles from weeks to days.

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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