# Parth Godse

> Software Engineer @ HCN · Full Stack Engineer · AI Engineer · Python · React · FastAPI · AWS | Open to New Opportunities

Location: San Francisco, California, United States
Profile: https://flows.cv/parthgodse

A software engineer who enjoys building full stack applications and also dabble into machine Learning once in a while with a interest in leveraging data to solve real-world problems. 

I have successfully completed my Master's in Computer Science and am proficient in Python, React, TypeScript, JavaScript, Tensorflow, Keras. My journey revolves around building and delivering solutions and deploying robust models, optimizing algorithms, and exploring new techniques in the field of Artificial Intelligence. With some experience in Computer Vision I am currently learning about LLMs and its various applications and creating different agents to tackle or automate daily problems

I am a firm believer in the power of teamwork, I've had the privilege of collaborating with brilliant minds in diverse projects. Let's connect and explore. I'm open to networking and discussing opportunities. Feel free to reach out.

## Work Experience
### Software Engineer @ Heartland Community Network
Jan 2026 – Present | United States
• Client had no single tool to manage their digital marketing, relying on disconnected platforms. Built a production SaaS platform from scratch with a React + TypeScript frontend, Node.js/Express backend, and PostgreSQL, handling end-to-end billing via Stripe and AI features via OpenAI.
• Owners had no visibility into why their restaurant was underperforming online across search, reviews, and social.
Engineered an AI-powered audit engine that analyzes Google Business, local SEO, review sentiment, social media, and competitors, surfacing a prioritized action plan in a single dashboard.
• Menu optimization required expensive consultants and days of manual analysis. Built an OCR-based menu ingestion pipeline with OpenAI extraction generates recommendations, and feeds a Canva-integrated design studio, cutting analysis time from hours to minutes.

### Software Engineer @ CNS - Indiana University Bloomington
Jan 2025 – Jan 2026
• Designed and shipped interactive graph-based dashboards for FTU anatomical data exploration using React and
AngularJS backed by Node.js on AWS, reducing researcher navigation time by 3x over the prior interface.
• Tightened RESTful API integrations with data normalization and validation, fixing recurring client-server mismatches and bringing frontend error rate down to under 0.1% across 13+ months of production use.
• Automated segmentation pipeline – Built an OpenCV + NumPy system to segment biomedical images, reducing manual effort from hours to minutes. Scaled processing to 400k+ images using CUDA + multithreading, cutting runtime by 40% while preserving image quality via adaptive resizing and aspect-ratio alignment.
• Anatomical visualization system – Developed a Python-based engine to auto-generate SVG schematics of organ blood flow from structured CSV datasets. Implemented ID normalization, FTU to FTU vascular inference, and custom Matplotlib rendering utilities with adaptive layouts, producing high-resolution, reproducible biomedical diagrams.

### Software Engineer - AI/ML @ Hyphenova Network
Jan 2024 – Jan 2024 | California, United States
• Built the full-stack sentiment analysis product: React dashboard consuming Flask and FastAPI endpoints backed by
Dockerised PyTorch models, raising transformer accuracy by 15% and throughput by 10% across 5+ units.
• Designed a RESTful API layer with Docker and Flask to decouple ML inference from the frontend, allowing each service
to be updated and scaled independently without breaking the UI.
• Launched a Kubernetes-based recommendation system with Prometheus and Grafana monitoring, achieving a 20% boost
in personalization efficiency and 25% higher anomaly detection accuracy.

### Machine Learning Engineer @ Indiana University Luddy School of Informatics, Computing, and Engineering
Jan 2024 – Jan 2024

### Software Engineer @ Canspirit Artificial Intelligence
Jan 2022 – Jan 2023 | Pune District
• Built a full-stack object detection platform: React frontend and Flask REST API serving a DETR model with a ResNet backbone, achieving 90% detection accuracy and meeting client requirements on the first production deployment.
• Configured KubeEdge clusters on WSL with Kubernetes to deploy lightweight AI models to edge devices, sustaining 81% model accuracy at minimal latency in low-connectivity environments and built a React dashboard for live inference monitoring.
• Optimized Docker image builds and deployment scripts, reducing inference response time by 15% across all target platforms.


## Education
### Master's degree in Computer Science
Indiana University Bloomington

### Bachelor of Technology - BTech in Computer Science
MIT World Peace University


## Contact & Social
- LinkedIn: https://linkedin.com/in/parth-godse
- Portfolio: https://www.pgodse.me/

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Source: https://flows.cv/parthgodse
JSON Resume: https://flows.cv/parthgodse/resume.json
Last updated: 2026-04-10