Aditya Singh
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// PRODUCTION DEPLOYMENTS & ARCHITECTURAL CASE STUDIES · SECTION 04

Flagship Systems & Production Impact

Technical architecture breakdowns of production systems, projects, and repositories this candidate has attached to their dossier.

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SYSTEM 01 // PROJECTRole: FEATURED PROJECT

Brahma — AI-Powered Creator Automation Platform

Architecting a multi-platform creator automation system using Next.js App Router, TypeScript, Supabase Auth, PostgreSQL, and server-side API routes handlers for YouTube and LinkedIn workflows. Engineered a multi-region YouTube ingestion and trend-ranking pipeline using views-per-hour, engagement rate, comment rate, momentum, freshness, opportunity score, and normalized global-reach signals. Built a channel growth analytics engine that calculates performance baselines, classifies videos as winners or under-performers, and generates data-driven content opportunities from historical channel metrics. Developed a modular AI Studio pipeline for script generation, voice synthesis, background music, scene planning, visual resolution, video rendering, and persistent project management.

TechnologyNext.js
StackTypeScript
PlatformSupabase
Metric 01—Pending
Metric 02—Pending
Metric 03—Pending
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SYSTEM 02 // PROJECTRole: FEATURED PROJECT

AI-Based Resource Allocation in NOMA for 5G and 6G Networks

Developed an original MATLAB simulation framework for downlink power-domain NOMA with superposition coding, successive interference cancellation, and adaptive power allocation. Implemented channel-aware greedy user pairing and a tabular Q-learning agent trained over 500 episodes to learn state-dependent power-allocation policies. Benchmarked fixed and intelligent NOMA against OMA under identical AWGN, Rayleigh, and Rician channel realizations using BER, throughput, spectral efficiency, energy efficiency, and Jain’s fairness index.

TechnologyMATLAB
StackQ-Learning
PlatformNOMA
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SYSTEM 03 // PROJECTRole: FEATURED PROJECT

PulseWatch - Cloud Observability and Incident Management Platform

Architected a distributed monitoring pipeline using Celery Beat, Redis queues, and background workers for scheduled website and API health checks. Built asynchronous FastAPI services with PostgreSQL to manage monitors, persistent check history, and automatic incident detection and recovery. Engineered rolling 24-hour uptime, average latency, and P95 latency analytics with real-time WebSocket updates. Containerized six application services using Docker Compose and implemented CI automation for linting, testing, security auditing, and image builds.

TechnologyFastAPI
StackNext.js
PlatformPostgreSQL
Metric 01—Pending
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// RECRUITMENT & ADVISORY ENGAGEMENTS

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Full verification packet containing source artifacts, production traces, and formal proofs available for prospective leadership roles once attested.

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