IIITM • CS • Grad ’26

Backend Engineering • Distributed Systems • Cloud Infrastructure • ML/GenAI • Competitive Programmer

Software Engineer focused on scalable backend architectures, distributed microservices, and ML-integrated platforms.
I build systems that emphasize fault-tolerance, observability, and performance under concurrency — not just features.

• Ex-Software Development Engineer Intern — AlgoUniversity (YC-Backed)
• McKinsey.org Forward Leadership Fellow
• 1145+ LeetCode | 81% Acceptance | 365-Day Consistency Badge
• Codeforces Expert — Max Rating 1850

My approach blends algorithmic rigor + system design thinking + cloud-native execution.

Primary Focus:
Backend Engineering • Distributed Systems • Reliability Engineering • ML / GenAI Platforms

May 2025 – Aug 2025 | Remote
Tech Stack: Node.js • Redis • gRPC • Docker • BullMQ • Terraform • GitHub Actions • OAuth2 • JWT

• Architected a Distributed Online Judge Platform serving ~500 active users with 110 peak concurrent executions
• Engineered event-driven evaluation pipelines using Redis Streams + gRPC, achieving ~5× throughput improvement
• Developed Docker-isolated code-execution microservice with BullMQ queues, CPU/memory sandboxing, and failure isolation
• Implemented JWT/OAuth2 authentication, RBAC authorization, and multi-layer caching to reduce latency and unauthorized access vectors
• Automated infrastructure provisioning & CI/CD pipelines via Terraform + GitHub Actions, enabling zero-downtime deployments
• Enforced stateless service design, idempotent job handling, and horizontal scalability patterns for high availability under concurrency

Aug 2025 – Present | Remote
Focus Areas: Analytics • Decision Frameworks • Automation • Business Intelligence

• Designed structured analytical pipelines and decision frameworks for complex problem-solving scenarios
• Applied MECE principles and hypothesis-driven engineering models to decompose ambiguous systems and optimize solution paths
• Built data dashboards and automation workflows using Python + BI tools, improving reporting clarity and operational efficiency
• Strengthened stakeholder communication, strategic thinking, and cross-functional collaboration in distributed team environments

Repository: https://github.com/Rajneesh180/Premium-Online-Judge

A production-grade full-stack coding platform inspired by LeetCode / Codeforces, engineered for concurrent code execution, real-time analytics, and scalable contest infrastructure.

Scale & Impact

• ~500 real users • 110 peak concurrent sessions
• Handles parallel submissions with isolated execution environments

Core Architecture & Capabilities

• Docker-isolated compiler microservice for secure sandboxed execution
• BullMQ asynchronous job queues with retry logic and failure isolation
• JWT Authentication + RBAC authorization layers
• Live contests, leaderboards, submission analytics & rating system
• Caching + stateless service design enabling horizontal scalability

Tech Stack: React • Node.js • MongoDB • Redis • Docker • AWS • Tailwind CSS

Repository: https://github.com/Rajneesh180/Event-Driven-API-Reliability

A self-hosted reliability and observability platform engineered for fault-tolerant API monitoring, automated recovery workflows, and infrastructure-as-code deployment.

Scale & Impact

• Designed for high-availability monitoring across distributed service endpoints
• Enables automated retries and alerting without manual intervention

Core Architecture & Capabilities

• Queue-based worker microservices enabling asynchronous health checks
• Retry, backoff, and cooldown logic to prevent alert storms
• Idempotent alerting pipelines ensuring duplicate-safe notifications
• Terraform-provisioned AWS infrastructure (SQS, DynamoDB, IAM, VPC)
• Observability-first design with structured logging, metrics, and tracing hooks

Tech Stack: Terraform • AWS SQS • DynamoDB • Docker • Microservices • Node.js

Repository: https://github.com/Rajneesh180/GestureTalk

A real-time machine learning inference platform enabling sign-language-to-text and gesture-to-speech conversion using computer vision and NLP pipelines.

Scale & Impact

• Achieved ~97% gesture recognition accuracy across curated ASL datasets
• Designed for low-latency inference suitable for live webcam streams

Core Architecture & Capabilities

• TensorFlow-based CNN models for real-time gesture classification
• OpenCV video processing pipeline with frame normalization and landmark extraction
• NLP-enhanced text-to-speech conversion for contextual phrase generation
• WebRTC streaming + Redis message buffering for low-latency communication
• Modular inference pipeline supporting model retraining and dataset expansion

Tech Stack: TensorFlow • OpenCV • Python • Flask • WebRTC • Redis • NLP

• LeetCode — 1145+ Problems Solved
  81% Acceptance • 365-Day Consistency Badge • 99%+ Performance Percentile
• Codeforces — Expert Tier | Max Rating: 1850
• Flipkart GRiD Hackathon — Advanced to Level 3 (Cleared 2 Coding Rounds)
• Amazon London Online Assessment — Qualified | Awaiting Interview
• Top 0.5% Performer — AlgoUniversity National Programming Camp

• Authored in-depth articles on System Design, Advanced DSA, and SOLID Principles
• Multiple publications exceeding 500+ reads, with sustained reader engagement
• Emphasis on visual intuition, diagrams, and real-world engineering analogies
• Covered topics include Binary Trees, Graph Algorithms, Load Balancers, Caching Strategies, and Distributed Systems
• Goal: translate complex architectures into practical, developer-friendly insights

“If it scales — it succeeds. Build with impact, not just intent.”