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Jayraj Pamnani

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

About

I am a Software Engineer with experience building full-stack applications, backend services, cloud infrastructure, and ML systems. Skilled in developing scalable applications, REST APIs, CI/CD pipelines, and cloud-native services using Python, Java, TypeScript, React, Kubernetes, and AWS/GCP.

Experienced in improving system performance, automating deployments, and delivering reliable production-ready solutions across software and ML environments.

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 and Algorithms, Operating Systems, Compiler Design, GPU Computing, Pattern Recognition.

Technical Skills

Languages
Python, Java, Go, TypeScript, SQL, Bash
Backend
FastAPI, Django, Flask, Node.js, REST APIs, Microservices
Frontend
React, Next.js, Tailwind CSS, shadcn/ui
Cloud & DevOps
AWS, GCP, Azure, Docker, Kubernetes, Terraform, GitHub Actions, CI/CD
AWS Services
EKS, Fargate, S3, SQS, RDS, CloudFront, ALB
Databases & Data
PostgreSQL, MongoDB, Snowflake, Kafka, Spark, Airflow
ML/AI
PyTorch, MLflow, Whisper, Coqui TTS, Model Serving

Professional Experience

Software Engineer — Meesho, Bengaluru, India Feb 2023 – Aug 2024
  • Owned the development and production optimization of real-time voice AI services, initially supporting voice-enabled search across 6 Indian languages and dialects before transitioning to conversational voice AI for customer support.
  • Designed and implemented latency optimization improvements across the voice pipeline by profiling end-to-end request paths, identifying network and service bottlenecks, deploying inference workloads across ~30 geographic regions, and co-locating STT, LLM, TTS, and orchestration services to reduce network round trips; helped reduce response latency from ~1.5s to sub-second performance.
  • Owned real-time responsiveness and turn-taking improvements by implementing streaming across STT to LLM to TTS, tuning service routing, and designing A/B tests for end-of-speech detection, reducing the response wait window from 1–2s to approx. 400–500ms while improving conversational flow and reducing the robotic effect.
Software Engineer — 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.
Teaching Assistant — Machine Learning — New York University, New York, NY Sep 2025 – May 2026
  • 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.
Software Engineering Intern — Swaroop.ai, Ahmedabad, India Oct 2022 – Jan 2023
  • Worked on voice AI systems for a creator platform, improving speech-to-text and text-to-speech pipelines for Indian languages, accents, and multilingual conversations, supporting approximately 10,000+ voice interactions per month.
  • Built and optimized speech-processing workflows including audio preprocessing, noise reduction, transcription, model evaluation, and inference, improving speech recognition accuracy by approximately 10–15% and reducing average processing latency by 20%.

Projects

  1. StreamForge (Distributed Video Transcoding & Secure Streaming Platform)
    Node.js, AWS (Fargate, S3, SQS, CloudFront), FFmpeg.
    Architected an event-driven, distributed video transcoding pipeline using AWS S3, SQS, and containerized FFmpeg workers on AWS Fargate with adaptive HLS streams (.m3u8/.ts). Engineered a secure, low-latency media distribution layer backed by AWS CloudFront CDN and signed cookies with RSA key pairs. Developed the full-stack web platform and embeddable HLS video player with React, TypeScript, and Tailwind CSS.
    [code]
  2. ActualBudget Transaction Categorizer
    Python, FastAPI, PostgreSQL, MLflow, Docker, Kubernetes, Terraform.
    Designed a reproducible ML platform around data ingestion, model evaluation, serving, experiment tracking, and GitOps-based infrastructure for categorization services. Built a Python-based data platform (API Backend) with PostgreSQL that ingests and categorizes financial transactions using a multi-stage ML classification pipeline. Engineered end-to-end data lifecycle management, CI/CD, and IaC for model serving and scaling.
    [code]
  3. HexDrop (Secure File Transferring WebApp)
    Next.js, TypeScript, Prisma, PostgreSQL, AWS, Docker, K8s.
    Developed a secure, full-stack file-sharing application using Next.js and TypeScript, implementing client-side encryption and managing the end-to-end user-facing flow. Designed a cloud-native deployment architecture on EKS with HPA, ALB Ingress, RDS PostgreSQL, External Secrets, and GitHub Actions to support secure deployments and horizontal scaling.
    [code]

A full list of repositories is available on GitHub.

Selected Certifications