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ShachiMistry/README.md

Hey there, I'm Shachi Mistry πŸ‘‹

Software Engineering Graduate | Backend & Distributed Systems | RAG Systems Builder | Python & SQL Enthusiast

πŸš€ I enjoy designing scalable architectures and building production-ready systems.
πŸ’‘ Constantly exploring new paradigms in Software Engineering, AI, and Distributed Computing.

About Me πŸ‘¨β€πŸ’»

I am a Software Engineer with a strong foundation in building scalable backends, microservices, and high-performance data architectures. My expertise lies in Distributed Systems, Cloud-Native Development, and AI-Driven Applications.

πŸ”Ή Core Engineering: βœ” Backend: Python (FastAPI/Flask), SQL βœ” System Design: Microservices, Event-Driven Architecture, REST/gRPC APIs

πŸ”Ή Cloud & Infrastructure: AWS, Kubernetes, Docker, Serverless, CI/CD Pipelines

πŸ”Ή Data & Distributed Systems: Apache Spark, Kafka, Snowflake, MongoDB, PostgreSQL

πŸ”Ή AI & Search: Retrieval-Augmented Generation (RAG), Vector Databases, LLM Integration

Projects & Experience πŸ› οΈ

βœ… LayoverOS – Autonomous AI Agent
Built a state-persistent recovery agent for stranded travelers using MongoDB Atlas Vector Search, LangGraph, and Python. Implemented complex state management and autonomous decision-making logic.

βœ… Distributed Data Platform – High-Scale Distributed System
Engineered a fault-tolerant platform processing 5TB+ daily with 100+ concurrent jobs using Airflow, PySpark, and FastAPI on Kubernetes. Designed for high availability (99.9% uptime) and horizontal scalability.

βœ… Triton Inference Monitor – System Performance & Observability
Developed a high-performance observability tool for NVIDIA Triton Server with automated latency profiling and hardware telemetry to optimize inference workloads.

βœ… Scalable Order Processing System – Event-Driven Microservices
Designed a cloud-native, event-driven system handling distributed idempotency and optimistic locking using FastAPI, AWS SQS, and DynamoDB. Focus on consistency and high throughput.

βœ… Netflix Popularity Prediction – Machine Learning Engineering
Created a predictive model and pipeline to analyze content trends using Machine Learning algorithms, focusing on feature engineering and model serving.

My Tech Stack πŸ”§

Connect with Me 🌐

Let's connect! You can find me on:

LinkedIn Email

πŸŽ€ Fun Fact: I paint, drink a lot of coffee, and write high-performance codeβ€”sometimes all in one evening! οΏ½πŸ’»βœ¨

Pinned Loading

  1. LayoverOS LayoverOS Public

    An autonomous, state-persistent recovery agent for stranded travelers. Powered by MongoDB Atlas Vector Search & LangGraph.

    Python 3 1

  2. data-platform data-platform Public

    Forked from aryaMehta26/data-platform

    Distributed data platform processing 5TB+ daily with 100+ concurrent jobs. Built with Airflow, PySpark, FastAPI on Kubernetes. 50+ DQ rules, 99.9% uptime, <100ms latency, 40% cost reduction.

    Python

  3. triton-inference-monitor triton-inference-monitor Public

    High-performance GPU inference benchmarking tool for NVIDIA Triton Server with automated latency profiling and hardware telemetry.

    Python 1

  4. Netflix_popularity_Predicition Netflix_popularity_Predicition Public

    Forked from aryaMehta26/Netflix_popularity_Predicition

    Decoding the Next Big Hit: How I Built a Crystal Ball for Netflix Content (Before It Airs!)

    Jupyter Notebook

  5. scalable-order-processing-system scalable-order-processing-system Public

    A cloud-native, event-driven order processing system built with Python (FastAPI) and AWS (SQS/DynamoDB). Demonstrates microservices patterns including distributed idempotency, optimistic locking, a…

    Python

  6. Stock-Market-ETL-Analytics-Platform Stock-Market-ETL-Analytics-Platform Public

    Built an end-to-end ETL and analytics platform to process real-time stock market data using Apache Airflow, Kafka, and Spark. Designed automated ingestion pipelines with data validation and stored …

    Python