Prashanna Raj Pandit

Graduate Research Assistant

Computer Science Student

Graduate Research Assistant at Southern Illinois University Edwardsville specializing in Data Analytics, ML, Statistical Modeling, and Deep learning. Passionate about leveraging AI for automation, pattern recognition, and solving real-world problems.

Services

Gen AI & RAG Pipelines

End-to-end Generative AI systems with Retrieval-Augmented Generation — document ingestion, vector indexing, multi-query retrieval, and grounded response generation. Production-grade domain-specific chatbots.

RAG LLM ChromaDB FastAPI Cohere Whisper

View Chatbot Catalog

ML / AI Development

Custom machine learning and deep learning solutions — time-series classification, computer vision models, NLP systems — built end-to-end from data preprocessing to trained, evaluated models.

TensorFlow PyTorch Scikit-Learn LSTM CNN OpenCV

Data Analysis & Visualization

Exploratory data analysis, statistical modeling, and interactive dashboards. Transforming raw datasets into actionable insights using Python and Tableau.

Pandas NumPy Matplotlib Seaborn Tableau R

MLOps & Deployment

Production-ready ML deployment with CI/CD automation, containerization, model versioning, and monitoring for scalable, reproducible inference pipelines.

Docker GitHub Actions MLflow Airflow CI/CD

Cloud Solutions

Cloud-native architectures on AWS — serverless functions, managed ML services, scalable data pipelines, and infrastructure automation for production workloads.

AWS SageMaker EC2 S3 Lambda ECR ECS

API Development

Production-grade REST APIs with FastAPI and Flask — modular, well-documented, and integrated with ML backends for real-time inference and data exchange.

FastAPI Flask REST Python PostgreSQL

Technical Skills

Programming Languages

Python R SQL C C++

Frameworks & Libraries

TensorFlow Scikit-Learn Keras OpenCV Optuna YOLO Flask Pandas NumPy SciPy Matplotlib Seaborn

Tools & Software

Git/GitHub Docker Tableau GitHub Actions Airflow AWS (S3, EC2) PostgreSQL Bash Linux Mlflow

Cloud Technologies

S3 EC2 SageMaker IAM Lambda ECR ECS CloudWatch CodePipeline/CodeBuild API Gateway SNS & SQS

Concepts

Object Oriented Programming Machine Learning Statistical Modeling Data Preprocessing Neural Networks/DL Time Series ML Pipeline Model Deployment CI/CD Cloud Services

Soft skills

Collaboration Communication Project management Leadership

Experience

Graduate Research And Teaching Assistant

Jan 2026 – Present

Southern Illinois University Edwardsville

• Edwardsville, IL, USA

Research in autism detection model and assist in CS 325, CS 584, and CS590 courses.

Assisted students in designing modular, object-oriented software systems, emphasizing SDLC best practices, clean architecture, and maintainable code. (CS 325)
Guided students in building CI/CD pipelines, Docker-based containerization, and REST API integration for production-style applications.(CS 325)
Mentored students in data processing pipelines, including data cleaning, feature engineering, exploratory data analysis, and model evaluation. (CS 584)

Research Assistant

Jan 2025 – Jan 2026

Edwardsville, IL, USA

Research in gait analysis and AI-based motion understanding using deep learning and signal processing.

Designed and implemented end-to-end data processing pipelines by extracting 2D pose keypoints from noisy video datasets using OpenPose; processed large-scale JSON data with Python (Pandas, NumPy), OpenCV, and Bash scripting to compute clinically relevant gait features such as gait cycle, stride length, cadence, and joint angles.
Applied statistical methods and feature engineering to gait time-series data, including cubic spline interpolation, Savitzky–Golay smoothing, gait phase segmentation, and turn removal; constructed a clean, multimodal dataset optimized for deep learning models and visualized gait patterns using Matplotlib and Seaborn.
Developed and trained sequential deep learning models (LSTM) for autism classification, achieving 88% accuracy, and performed model optimization through validation, class balancing, and performance analysis.
Packaged trained machine learning models using Docker and deployed them through RESTful APIs, integrating them into a production-ready inference pipeline with CI/CD automation to support scalable, repeatable deployments and seamless integration with a web-based application.

Machine Learning Engineer

Jul 2024 – Jan 2025

Vrit Technologies

• Kathmandu, Nepal

Built and shipped end-to-end ML pipelines in production - data ingestion, feature engineering, model training, and REST API deployment on AWS - cutting release time by 40% through CI/CD automation (GitHub Actions, Docker)
Engineered automated ETL pipelines from multiple heterogeneous data sources with SQL-driven transformations, reducing manual data processing time by 60% and enabling real-time analytics
Deployed ML models as production-grade REST APIs using Python and Flask, enabling real-time inference and embedding ML intelligence directly into web applications.
Implemented monitoring, logging, and performance tracking for deployed ML services, ensuring system reliability and faster debugging in production

Software Engineer

Jan 2023 – Jul 2024

Kathmandu, Nepal

Full-stack web development and backend optimization for scalable AI-ready systems.

Built and deployed a Flask-based web application with RESTful APIs, leveraging SQL-backed structured data models and JSON-based data exchange to support analytics, reporting, and backend business logic; improved system performance and SEO metrics by 20%.
Designed and maintained data workflows using SQL queries for data retrieval, transformation, and validation, enabling reliable storage, querying, and integration of application data across services.
Implemented CI/CD pipelines using GitHub Actions and Docker in a Linux environment, automating testing and deployment and reducing manual release effort by 40%, while collaborating with the team using Git and Bash scripting.

Certifications