Highlights

A collection of engineering challenges I have tackled recently, spanning from distributed container orchestration to high-concurrency booking engines and enterprise RAG systems.

HGPT: Enterprise RAG Knowledge Base

An enterprise AI search engine that allows users to securely query 10,000+ internal documents in real-time, built with Llama 3 and a custom Retrieval-Augmented Generation (RAG) pipeline.

What it does

•Implemented a hybrid search strategy using ChromaDB, BM25 for semantic vector retrieval, and Reciprocal Rank Fusion.
•Cross-Encoder re-ranking to maximize context relevance, improving accuracy by 50% over standard keyword search.
•Enabled high-performance ingestion pipeline using FastAPI and Celery workers to asynchronously process and chunk large datasets into embedded vectors with Chain-of-Thought reasoning without blocking the UI.

RoleFull-stack AI

FocusSemantic Search

PythonLangChainChromaDBOllamaReactFastAPICelery

Deploy: Distributed Container Orchestration Engine

A custom container orchestration platform, similar to a lightweight Kubernetes, designed to automatically monitor, scale, and recover failing server nodes in under 30ms.

What it does

•Reduced node failure detection time to <30ms by engineering a custom distributed control plane in Go.
•Built a CLI and Dashboard utilizing Grafana to visualize real-time node metrics, exposing cluster state via RESTful API.
•Ensured cluster consistency and crash recovery by implementing a Write-Ahead Log (WAL) in PostgreSQL, creating a robust failover system for controller outages.

RoleSystems Engineer

StatusCompleted

FocusCluster Resilience

GoPostgreSQLDockerREST APIGrafanagRPCPrometheus

Shuttle: High-Concurrency Transit Reservation System

A scalable booking engine handling high-concurrency seat reservations across web and mobile platforms.

What it does

•Reduced double-booking conflicts by 95% during burst traffic events by architecting a booking engine utilizing Redis Distributed Locks (Redlock) and Optimistic Concurrency Control (OCC).
•Secured PCI-compliant payments across React web and React Native mobile interfaces by integrating Stripe API webhooks within serverless functions for asynchronous verification.
•Scaled real-time inventory updates to 1,000+ concurrent clients with MongoDB versioning to manage seat inventory state.

RoleBackend Lead

FocusConcurrency Control

Node.jsReactReact NativeRedisMongoDBStripe APIAWS Amplify

E-Commerce GraphQL Architecture

Led redesign of backend GraphQL services for a high-traffic e-commerce platform serving 50k+ daily requests at Speeler Technologies.

What it does

•Reduced P95 latency by 30% by implementing resolver batching and AppSync caching.
•Designed a multi-tenant DynamoDB Single-Table Architecture, utilizing sparse indexes and GSI sharding to eliminate hot partitions and support high-cardinality access patterns.
•Engineered a fault-tolerant, event-driven pipeline using Lambda and SQS Dead Letter Queues (DLQ) to process images, reducing operational infrastructure costs by 70%.

RoleBackend Engineer

StatusProduction

FocusGraphQL scaling

AWS AppSyncGraphQLDynamoDBAWS LambdaS3ReactAWS ECSCognito

InterviewPrep: Full Stack Quiz App

A comprehensive learning platform allowing users to master core programming topics through interactive quizzes and curated study materials.

What it does

•Hyper-detailed, structured learning content across 10 core programming topics (DSA, OOPS, React, etc.).
•Topic-wise interactive quizzes with randomized and All-in-One testing modes.
•Modular NestJS backend providing dedicated REST endpoints for learning content and question retrieval.

RoleFull Stack Engineer

StatusCompleted

FocusEducation / Assessment

ReactTypeScriptNestJSTailwind CSSAxios

Peeppa: Price Tracker Engine

A cross-retailer product scraping engine that automatically tracks price drops across major outlets like Amazon, Best Buy, and Target.

What it does

•Real-time and historic price tracking across automated scrapers spanning diverse e-commerce structures.
•Dynamic threshold-based email alerts triggering notifications when products drop below target prices.
•Historical pricing charts mapping price fluctuations dynamically from MongoDB.

RoleBackend Engineer

StatusCompleted

FocusWeb Scraping / Data Engineering

PythonFlaskMongoDBBeautifulSoupHTML/CSS

2D Interactive Maze Game

A 2D interactive maze game built using Python and Tkinter that generates a new solvable maze every run using Depth-First Search (DFS).

What it does

•Procedural maze generation using recursive backtracking (DFS).
•Guaranteed solvable maze from start to end with win detection and smooth frame-based updates (~60 FPS).
•Grid-based coordinate-to-pixel transformation supporting dynamic scaling.

RoleSoftware Developer

StatusCompleted

FocusProcedural Generation

PythonTkinterAlgorithmsDFS

RawhPlayer: Desktop Media Player

A lightweight desktop media player application focused on custom UI design and core playback functionality.

What it does

•Audio playback controls (Play / Pause / Stop) with manual file handling and media loading.
•Modular code structure separating event-driven UI components from core logic.
•Tight state management across playback transitions (idle → playing → paused).

RoleSoftware Developer

StatusCompleted

FocusUI Design / Media Handling

PythonPygameGUI Design

Facial Expression Recognition with PyTorch

A machine learning project designed to accurately classify human facial expressions from images using a trained PyTorch model.

What it does

•Preprocessing of facial images for robust feature extraction.
•Training a convolutional neural network (CNN) for precise expression recognition.
•Achieving high accuracy in classifying diverse expressions such as happiness, sadness, anger, and surprise.

RoleMachine Learning Engineer

TimeJun 2024 – Jun 2024

StatusCompleted

FocusComputer Vision / Deep Learning

PyTorchPythonDeep LearningCNN

Image Classifier using Tensorflow

A comprehensive deep learning model building experience producing high validation accuracy by classifying images into organized folders.

What it does

•Enhanced the dataset with robust data augmentation techniques to drastically improve model accuracy and robustness.
•Built and trained a convolutional neural network (CNN) with optimized layers and dropout regularization.
•Developed an automated pipeline to actively classify images and dynamically move them into respective category folders based on predictions.

RoleMachine Learning Engineer

TimeJun 2024 – Jun 2024

StatusCompleted

FocusImage Classification

TensorFlowKerasPythonGoogle ColabCNN