Hello, I'm Ojas
Software Engineer
I have 4+ years of experience building scalable, distributed systems in fast-paced Agile environments. Proven track record developing microservices and full-stack applications using Java, Kotlin, Python, Spring Boot, React, Kafka, ElasticSearch, SQL, and NoSQL databases, plus LLMs (LangChain/RAG) and agentic AI with MCP servers. Skilled in designing secure REST APIs, implementing real-time data pipelines, and integrating observability tools like OpenTelemetry and Prometheus. Hands-on with CI/CD, Jenkins, Docker, Kubernetes, and AWS-based deployments. Passionate about clean code, test-driven development, and delivering reliable software at scale.
Professional Experience
Full-Time:
Internships:
Certifications
About Me
Get to know me better, my journey, and what drives me
My Journey
I kicked off my tech journey with a Bachelor's in Computer Engineering from COEP, with an internship at Mastercard, then dove into the industry with roles at IDeaS Revenue Solutions , and Amdocs, where I built everything from distributed backend systems to full-stack applications. Wanting to deepen my expertise, I pursued a Master's in Computer Science at San Jose State University, graduating with a 4.0 GPA. Most recently, I interned at Foundry Digital, working on full-stack tools in the crypto infrastructure space. Along the way, I've picked up a love for clean architecture, real-time systems, and learning whatever new tech the job throws at me.
Highest Education
M.S. in Computer Science
Location
San Jose, CA
Experience
4.5 Years
Passion
Creative Problem Solving
My Skills
Languages
Frameworks & Libraries
Cloud & DevOps
Database Technologies
Featured Projects
A selection of my recent work and personal projects
Spartan MyCompanion
- Developed a full-stack student event networking portal hosted on AWS Amplify, supporting 500+ concurrent users, with automated testing and deployment via GitHub Actions.
- Integrated AWS API Gateway, Lambda and DynamoDB sink for functionalities like event posting, replies, and voting, with real-time chat rooms using WebSocket API and event notifications using fanout with AWS SNS.
- Introduced infrastructure as code with AWS CloudFormation and JSON templates for automated deployment.
5G Handover Prediction System
- Engineered a machine learning pipeline using TensorFlow, Scikit-learn, Pandas, and NumPy to process 103K+ 5G network samples.
- Deployed an LSTM signal predictor and a CNN-BiLSTM hybrid classifier to predict cellular handover trigger events.
- Resolved a 47:1 class imbalance using Temporal SMOTE with 50-timestep sliding windows, achieving 98.37% prediction accuracy.
Let's Work Together
Have a project in mind or want to collaborate? I'd love to hear from you!