Hey there!
I'm Yash ā a software engineer who builds things that matter. From backend systems handling real-world data to on-device ML that runs in your pocket, I love turning complex problems into elegant solutions. If you're looking to collaborate or just want to chat about tech, let's connect š
<Yash Salunke />
Software Engineer Ć ML Engineer
Building scalable backend systems, intelligent ML solutions, and native iOS apps with precision engineering.
About Me
Iām a software engineer who builds dependable, user-facing systems across backend engineering, mobile development, applied machine learning, and data science.
My work spans production systems and research-driven projects, with a focus on clean system design, data quality, and practical AI integration.
I enjoy translating ideas into scalable software from backend services and APIs to mobile interfaces and data-driven components and collaborating to turn ambiguous requirements into robust, well-tested systems.
ā career
Work Experience š¼
Research / Software Engineer
Johns Hopkins University Jan 2024 ā Present- Built and maintained a cloud-based research platform for driving hazard perception studies
- Designed backend experiment logic and data pipelines for high-fidelity behavioral data
- Implemented real-time data processing and analysis workflows using Azure and Python
Data Science Intern
CCC Intelligent Solutions May 2023 ā Dec 2023- Built a real-time iOS inspection pipeline using on-device GenAI (FastVLM)
- Implemented frame extraction, blur filtering, and ML-driven retake workflows
- Developed SwiftUI applications with Core Motion integration and AWS Bedrock
Data Analyst Intern
Nasdaq Jan 2024 ā Jun 2024- Developed automated data pipelines and forecasting models for market analysis
- Built dashboards that significantly reduced reporting time
- Won Nasdaq Hackathon for developing innovative trading solutions
Data Science & Analytics Intern
Six Ladders Pvt. Ltd. Jun 2023 ā Aug 2023- Built analytics dashboards and churn prediction models
- Improved data retrieval performance and user retention
- Implemented Python-based ETL pipelines for data processing
ā check out some
Projects š»
Hazard Perception Research Platform
Johns Hopkins University
Tech Stack: Azure, Java, Python, CI/CD
- Cloud research platform for behavioral studies
- Backend experiment logic + data pipelines
- Real-time analytics + automated reporting
360Ā° Car Inspection System
CCC Intelligent Solutions
Tech Stack: SwiftUI, Core Motion, AWS Bedrock, Docker
- On-device GenAI pipeline for real-time inspection
- ML-driven frame quality + retake guidance
- Integrated capture app with cloud processing
š Internal / Private Project
Metabolic Syndrome Prediction
Research Publication
Tech Stack: Python, XGBoost, SHAP, Machine Learning
- ML study across 9 models on 2,400 patients
- XGBoost achieved 95% training and 88.97% testing accuracy
- Published in Procedia Computer Science (Elsevier)
LLM-BPE-BERT
Academic Project
Tech Stack: Python, PyTorch, NLP, Transformers
- Custom BERT model with BPE tokenizer
- Implemented from scratch for NLP tasks
- Training + evaluation pipeline for benchmarks
VoiceYourID
Security Project
Tech Stack: Python, GMM, MFCC, React, PWA
- Voice recognition system with 88% accuracy
- Implemented Gaussian Mixture Models for speaker identification
- Progressive Web App for cross-platform access
Real-Time Options Chain
Market Analysis Engine
Tech Stack: Java, Node.js, Angular, WebSockets, REST APIs
- Real-time market data processing and analysis
- WebSocket-based live streaming architecture
- Full-stack financial analytics dashboard + APIs
ā what i do best
Core Expertise šÆ
Backend & Systems Engineering
I design and build scalable backend systems with a focus on correctness, clean APIs, and real-time data handling. My work spans distributed systems, microservices, and cloud infrastructure.
Machine Learning & Applied AI
I apply machine learning to solve practical problems across healthcare, computer vision, and NLP. I care about model evaluation, interpretability, and data quality.
Mobile & On-Device Systems
I build native iOS applications with emphasis on performance and real-time processing. My work integrates sensors, on-device ML workflows, and backend systems.
Cloud Architecture & DevOps
I architect cloud solutions on AWS and Azure with automated CI/CD pipelines. From containerization to infrastructure-as-code, I ensure reliable deployments.
Research & Data Engineering
I build systems where engineering decisions impact research outcomes. From designing experiments to optimizing pipelines, I focus on technical choices that support analysis.
Full-Stack Development
I develop end-to-end solutions with modern tech stacks including React, Node.js, and Python. My work bridges frontend experiences with robust backend architectures.
ā research
Publications š
Metabolic Syndrome Prediction
Procedia Computer Science (Elsevier) ICMLDE 2023, Volume 235, 2024Evaluated 9 machine learning classifiers on 2,400-patient dataset. XGBoost achieved 95% training and 88.97% testing accuracy with 0.913 F1 score, identifying waist circumference as the most predictive biomarker.
Smart City IoT Research
IEEE Conference Publication 2024Research on smart city technologies and Internet of Things applications. Focused on developing intelligent systems for urban infrastructure and connectivity.
ā detailed resume
Resume š
Technical Skills
Data Science Intern - CCC Intelligent Solutions, United States
- Developed a SwiftUI iOS app for 360Ā° vehicle video capture, integrating Core Motion, GPS, and REST APIs for real-time analytics
- Deployed on-device damage detection using AWS Bedrock, Java microservices, and MemoryDB, improving inference accuracy by 22%
- Optimized frame extraction and streaming (2 FPS) pipelines via Dockerized services, reducing poor captures by 40% and improving ML data quality
Software Engineer (Part-Time) - Johns Hopkins University, United States
- Built and automated Azure CI/CD pipelines with GitHub Actions, cutting deployment time by 60% and improving uptime
- Refactored backend APIs in Java and Python, enhancing caching and query efficiency; improved response latency by 50%
- Tuned MongoDB/SQL indexing and implemented telemetry dashboards with Grafana, sustaining 99.9% uptime under load
Student Worker (Analyst) - Nasdaq, India
- Validated and tested Java-based trading applications, reducing post-release defects by 20%
- Analyzed market trends, partnering with PMs to define features, driving a 15% increase in user engagement
- Managed SQL database workflows and contributed UI enhancements that improved user satisfaction by 25%
Software Developer Intern - Six Ladders Pvt Ltd., India
- Built Node.js + React modules with AWS RDS backend, boosting performance and scalability by 30%
- Refined SQL operations on AWS RDS, ensuring data integrity and reducing retrieval time by 40%
- Designed and launched company website, enhancing user experience by 35%
Master of Science in Computer Science - Johns Hopkins University
GPA: 3.8/4.0
Coursework: Natural Language Processing, Object Oriented Programming, Intro to Algorithms, Deep Learning, Machine Learning
Bachelor of Technology in Computer Engineering - KJ Somaiya College of Engineering, Mumbai
GPA: 9.33/10.0 | Honours in Data Science
Coursework: Data Structures, OOP, Advanced Database, Algorithms Design, Cloud Computing
Hazard Perception Research Platform - Johns Hopkins University
Tech Stack: Azure, Java, Python, CI/CD
- Built a cloud-based research platform to support large-scale behavioral studies
- Designed backend experiment logic and data pipelines for accurate data collection
- Enabled real-time analytics and automated reporting to support research analysis
360Ā° Car Inspection System - CCC Intelligent Solutions
Tech Stack: SwiftUI, Core Motion, AWS Bedrock, Docker
- Developed an iOS pipeline using on-device GenAI for real-time vehicle inspection
- Implemented ML-driven frame quality assessment to improve capture reliability
- Integrated with cloud-based processing services for scalable analysis workflows
Metabolic Syndrome Prediction - Research Publication
Tech Stack: Python, XGBoost, SHAP, Machine Learning
- Conducted a comparative ML study across 9 models on a 2,400-patient clinical dataset
- Applied XGBoost with SHAP-based interpretability for explainable healthcare predictions
- Published results in Procedia Computer Science (Elsevier)
LLM-BPE-BERT - Academic Project
Tech Stack: Python, PyTorch, NLP, Transformers
- Built a custom BERT-based pipeline using Byte Pair Encoding (BPE) tokenization
- Implemented and evaluated models for downstream NLP tasks and semantic analysis
- Achieved competitive performance across benchmark datasets
VoiceYourID - Security Project
Tech Stack: Python, GMM, MFCC, React, PWA
- Developed a speaker identification system using Gaussian Mixture Models and MFCC features
- Achieved 88% accuracy in voice-based authentication experiments
- Built a Progressive Web App to support cross-platform access and deployment
Winner ā Nasdaq Hackathon Innovative Trading Solutions
2nd Place ā Edelweiss Market Hackathon Financial Technology
2 Published Research Papers Elsevier & IEEE
20+ Completed Projects Backend, iOS, ML, Full-Stack
ā let's connect
Get In Touch š¬
Open to new opportunities and collaborations. Feel free to reach out!