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Jayraj Pamnani

M.S. Computer Engineering, New York University

Tandon School of Engineering · May 2026

Jayraj Pamnani
jmp10051@nyu.edu · LinkedIn · GitHub · Resume

About

I recently graduated with a Master’s in Computer Engineering from New York University’s Tandon School of Engineering. My work sits at the intersection of machine learning, deep learning, and systems engineering, with a focus on building practical, efficient, and scalable AI systems.

I have experience developing and optimizing AI pipelines across model training, evaluation, deployment, and MLOps. My interests include backend engineering, AI infrastructure, cloud-native systems, and making large-scale models more reliable and production-ready.

Prior to NYU, I completed my B.Tech. in Computer Science & Engineering with a specialization in Artificial Intelligence at Parul University in India, where I built a strong foundation in data structures, pattern recognition, machine learning, and GPU computing.

Education

M.S. Computer Engineering — New York University, Tandon School of Engineering May 2026

Coursework: High Performance Machine Learning, Machine Learning Operations, Deep Learning, Database Systems, Big Data.

B.Tech. Computer Science & Engineering (AI Specialization) — Parul University, India May 2024

Coursework: Data Structures & Algorithms, Machine Learning, Deep Learning with NLP, Pattern Recognition, Image Processing, GPU Computing, Data Visualization.

Technical Skills

Languages
Python, SQL, C/C++, Java, JavaScript
ML/AI Frameworks
TensorFlow, PyTorch, Matplotlib, Seaborn, Scikit-learn, Neural Networks, Computer Vision, NLP
Data & Cloud
Distributed Systems, AWS, CI/CD, MongoDB, PostgreSQL, Hadoop, Spark, ETL Pipelines, Tableau Dashboards
Tools
Git, Docker, Kubernetes, Jupyter, Gradio, Hugging Face, WandB, n8n, Postman, LangChain

Professional Experience

Software Engineering Intern — GBCS Group, Calgary, Canada Nov 2025 – Feb 2026
  • Implemented and optimized cloud infrastructure using Infrastructure as Code (IaC) to improve resource utilization, resulting in a 28% reduction in hosting and maintenance costs.
  • Improved CI/CD pipelines and deployment workflows, accelerating release cycles by 40% while maintaining 99.9% system availability.
  • Helped redesign microservice boundaries and deployment workflows across a 5-engineer team, reducing technical debt, improving maintainability by 30%, and establishing patterns for future service development.
  • Defined deployment standards for Docker/Kubernetes-based services, improving release reliability and reducing manual intervention.
Freelance Software Developer — New York, NY Jul 2025 – Sep 2025
  • Engineered a Django-based web app automating data transfer between QuickBooks and KatanaMRP, cutting manual bookkeeping by ~85%.
  • Built a robust REST API integration using Django (backend) and JavaScript (frontend), enabling seamless synchronization of 10,000+ financial and inventory records with zero data loss.
  • Designed and implemented 5+ custom verification layers, achieving 99.8% data accuracy before syncing to KatanaMRP. Managed full project source code using Git and GitHub, maintaining clean version history and enabling reliable deployments to AWS EC2.
Teaching Assistant — Machine Learning — New York University, New York, NY Sep 2025 – Dec 2025
  • Guided 50+ graduate students through ML fundamentals, including preprocessing pipelining, supervised/unsupervised learning, Deep Learning, and model optimization techniques.
  • Conducted weekly office hours to debug Python code, explained algorithms, taught ML topics, and assisted with PyTorch implementations.
AI Intern — Swaroop.ai, Ahmedabad, India Mar 2024 – Aug 2024
  • Enhanced TTS model performance by 25% through fine-tuning Coqui TTS across multiple Indian languages.
  • Improved STT accuracy by 18% via domain-specific audio datasets and custom preprocessing.
  • Integrated OpenAI Whisper into production, increasing transcription accuracy on noisy data by 30% and cutting inference latency by 20%.
Data Scientist Intern — Robotskull, Vadodara, India Oct 2023 – Mar 2024
  • Processed 100K+ sales and inventory records; built demand forecasting models achieving 92% prediction accuracy.
  • Automated weekly analytics dashboards, reducing manual reporting time by 70%.
Teaching Assistant — Parul University, Vadodara, India Oct 2022 – Mar 2023
  • Supported 120+ students in OS concepts and C-based system programming; led weekly lab sessions on Linux programming and multithreading.

Projects

  1. ActualBudget Transaction Categorizer
    Python, FastAPI, PostgreSQL, MLflow, Docker, Kubernetes, Terraform.
    Built and deployed an ML-powered transaction categorization platform with data ingestion, experiment tracking, FastAPI serving, PostgreSQL storage, and GitOps-based infrastructure for reproducible model updates.
    [code]
  2. HexDrop (Secure File Transfer)
    Next.js, TypeScript, Prisma, PostgreSQL, AWS, Docker, K8s.
    This secure file-sharing application enables encrypted uploads and key-based downloads using AWS S3 and PostgreSQL.
    The platform operates on a full-stack DevOps pipeline featuring EKS orchestration, automated CI/CD, and scalable cloud infrastructure.
    [code]
  3. EyeConnect: Accessible Video Communication with AI-Powered Vision Assistance
    WebRTC, Supabase, OpenRouter AI, React, TypeScript.
    Accessibility platform connecting blind users with sighted volunteers via real-time video calls and AI vision assistance. Awarded 2nd place at NYU Hacks 2025.
    [code]
  4. Vision Transformer Optimization via Quantization & Efficient Attention
    PyTorch, bitsandbytes, FlashAttention-2, LoRA.
    Optimized ViT-L/16 using 4-bit/8-bit quantization and FlashAttention-2, achieving 4× model size reduction and 40% lower latency with minimal accuracy loss.
    [code]
  5. Model Merging for Large Language Models
    Python, PyTorch, Hugging Face, Google Colab.
    Implemented TIES and SLERP merging techniques to combine Mistral-7B variants with optimized hyperparameters for cross-task generalization.
    [code]
  6. Command Line Helper: Natural Language to Bash via Local LLM
    Converts natural-language instructions into bash commands using a local LLM and RAG-powered context retrieval, with both CLI and web interfaces.
    [code]
  7. Chapter: Secure Library Management System
    Python (Django), Oracle Data Modeler, HTML/CSS/JS, Oracle DB.
    Library management web application with role-based authentication, SQL-injection protection, and an employee dashboard for business metrics.
    [code]

A full list of repositories is available on GitHub.

Selected Certifications