SYSTEM_STATUS // OPERATIONAL
LOC: INDIA // LAT: 26.2183° N // LON: 78.1828° E
ARCH LINUX // IIITM GWALIOR

Gagan
Ahlawat

Software Development Engineer & Systems Researcher · SDE Intern @ Inforida Technologies. Specialized in high-concurrency backend architecture, RF wireless signal processing (CrossNet-MIMO), and open source MCP integration layers.

gagan@archlinux: ~ (zsh)
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About & Core Ethos

ETHOS STATEMENT
"I don't ship what I don't understand."

Dual Degree (B.Tech IT + MBA) student at ABV-IIITM Gwalior. SDE Intern at Inforida Technologies and Research Intern at Department of Management Studies. Specialized in high-concurrency backend architecture, MIMO channel estimation neural models (CrossNet-MIMO), and open source MCP integration layer.

ACADEMICS
IIITM Gwalior
CGPA 8.63 / 10
DEGREE
B.Tech IT + MBA
Dual Degree (2024 — 2029)
CURRENT ROLE
SDE Intern
@ Inforida
KEY METRICS
  • Backend RBAC Tiers4 Levels
  • LeetCode Solved300+
  • LeetCode Rating~1496
  • SAC TenureSecretary
⚡ Focused on low-level systems, high concurrency, and non-leaky abstractions.
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Engineering & Leadership Sequence

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Featured Systems & Projects

FEATURED SYSTEM

CodeLink

Real-time Mobile Code Review & VS Code Extension Relay

Real-time mobile code review relay and VS Code pairing platform with token-based handshakes and zero message loss under concurrent load.

Sync Latency
< 100ms
Delivery Guarantee
100%
Security
QR & Token Handshake
  • ›Designed a Socket.IO relay for real-time mobile code review, achieving sub-100ms sync latency.
  • ›Implemented token-based handshake and QR-code pairing, blocking 0-tolerance unauthorized access attempts.
#TypeScript#Node.js#Socket.io#VS Code Extension API#React Native
Explore Technical Case Study
FEATURED SYSTEM

CloudOptRL

4D MDP Reinforcement Learning Cloud Allocation

Cloud resource allocation modeled as a 4D Markov Decision Process with property-based test validation.

MDP Model
4D State / 3 Actions
Target Band
40–70% Utilization
EpisodeGrader
30/30/40% Weighted
  • ›Modeled cloud resource allocation as a 4D Markov Decision Process with 3 discrete actions.
  • ›Formulated a normalized reward function balancing a 40–70% target utilization band against cost.
#Python#PyTorch#Gradio#NumPy#Hypothesis
Explore Technical Case Study
FEATURED SYSTEM

OllamaTUI

Arch-Native Terminal UI for Local LLM Orchestration

Developer-first terminal interface for local LLMs powered by Go, Bubble Tea, and non-blocking token streaming.

Response Latency
~60% Reduction
Hosted Models
5+ Models
Concurrent Sessions
10+ Parallel
  • ›Shipped a developer-first TUI for local LLM inference, supporting 5+ hosted models via Ollama.
  • ›Cut perceived response latency by ~60% with non-blocking token streaming via Go goroutines.
#Go#Cobra#Bubble Tea#Lip Gloss#Ollama API
Explore Technical Case Study
FEATURED SYSTEM

CrossNet-MIMO

Multi-Task MIMO Channel Estimation Neural Network

Packed multi-task feature extraction architecture for 64-antenna MIMO channel estimation and localization.

Parameters
4.8M Params
NMSE Score
0.47 – 0.52
Localization Gain
3–5x Error Cut
  • ›Developed CrossNet-MIMO, packing multi-task feature extraction into a 4.8M-parameter model.
  • ›Trained MIMO channel estimation models on 10,000+ samples, driving NMSE down to 0.47–0.52.
#PyTorch#NumPy#MATLAB#MIMO Wireless
Explore Technical Case Study

// Additional Engineering Projects

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Research & Open Source Contributions

Research & Technical Papers

MIMO Wireless & Deep LearningDept. of Management Studies, ABV-IIITM Gwalior

CrossNet-MIMO Channel Estimation & Localization Model

Deep learning multi-task feature extraction architecture for MIMO wireless channel estimation.

  • Developed CrossNet-MIMO, packing multi-task feature extraction into a 4.8M-parameter model.
  • Trained MIMO channel estimation models on 10,000+ samples, driving NMSE down to 0.47–0.52.
  • Optimized the localization pipeline, cutting estimation error by 3–5× over baselines.
Reinforcement LearningCloud Computing Research

4D MDP Cloud Resource Allocation Engine (CloudOptRL)

Reinforcement learning agent optimizing cloud utilization with property-based test validation.

  • Modeled cloud resource allocation as a 4D Markov Decision Process with 3 discrete actions.
  • Formulated a normalized reward function balancing a 40–70% target utilization band against cost.
  • Constructed an EpisodeGrader with 30/30/40% weighted scoring, validated via property-based tests.

Open Source Ecosystem

krkn-aiRed Hat / CNCF

Resolved setup.py dependency bug blocking pip installs (#360 fix) and migrated dependency management to pyproject.toml for Python 3.12 support across 3 merged PRs.

Role: Open Source ContributorRepository
GitMeshLinux Foundation

Built global repository search feature extending discovery to any public repo, delivered the MCP integration layer, and fixed 3 UI bugs across a 500+-commit codebase.

Role: Open Source ContributorRepository
COMMUNITY ENGAGEMENT

Active contributor to Linux systems tools, security benchmarking scripts, and Python optimization libraries.

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Technical Skill Matrix & Telemetry

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Live GitHub & LeetCode Metrics

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Direct Telemetry & Email Trigger

DIRECT_TELEMETRY
[TERMINAL CONSOLE LOG STREAM]REALTIME_TELEMETRY
[SYS_INIT]❯ [INFO]Contact daemon initialized. TLS 1.3 socket connected.
[SYS_READY]❯ [INFO]Fill in payload telemetry below to dispatch signal.
DIRECT MAIL PIPELINE // RESEND API INTEGRATION
RESP_TIME: < 24h // PGP KEY AVAILABLE UPON REQUEST