AEGIS_OS v2.0.4
USR: DIVIK.DHIMAN
[MOD.01] // IDENTITY

OPERATOR_PROFILE

SYS.USER:

DIVIK
DHIMAN

Focused on building reliable web systems and low-level software. I bridge modern full-stack development with systems-level understanding, prioritizing correctness, performance, and clear architecture over unnecessary complexity.

SYS.STATUS ONLINE WHEN YOU NEED
PRIMARY_DIRECTIVESOFTWARE ENGINEER
LOCGLOBAL
[MOD.02] // DIRECTIVE

CORE_OBJECTIVES

>

I am a software developer with experience in backend, full-stack, and systems-oriented projects. I work with databases, APIs, MCP-style backends, and AI systems, with a focus on building software that is reliable, efficient, and easy to reason about.

>

I design backend APIs and services with attention to performance, data modeling, and scalability. I am interested in working on systems where architectural choices directly impact correctness, efficiency, and long-term maintainability.

>

Currently seeking roles that demand rigorous engineering standards and complex problem-solving.

[MOD.03] // DEPLOYMENTS

ARCHITECTURES

SYS.01

LLM Hallucination Analysis / RAG Evaluation System

PythonRAGFAISS / ChromaBM25LLM APIsNLP Evaluation

PROBLEM_SPACE

Large Language Models frequently generate confident but incorrect responses when operating without grounded context, limiting reliability in production systems.

ARCHITECTURE

Designed and executed a controlled evaluation pipeline comparing direct prompting vs Retrieval-Augmented Generation (RAG) across 500+ queries. Implemented dense, BM25, and hybrid retrieval strategies with ablation studies on chunking methods and top-k selection. Built a reproducible framework to measure hallucination rate and answer faithfulness.

IMPACT_METRICS

Achieved 27% reduction in hallucination rate using optimized hybrid retrieval. Established a structured evaluation methodology for measuring LLM reliability and grounding effectiveness.

SYS.02

AI Tooling & MCP Server Suite

Node.jsPythonPostgreSQLModel Context Protocol (MCP)REST APIsAI Integration

PROBLEM_SPACE

AI systems require structured, validated access to real-world data sources to enable reliable reasoning and cross-domain querying.

ARCHITECTURE

Developed a suite of Model Context Protocol (MCP) servers enabling AI interaction with external systems including GitHub, Reddit, personal finance data, and a predictive ML model. Designed standardized tool schemas, secure REST API integrations, validated data pipelines, and robust input validation to support extensible AI workflows.

IMPACT_METRICS

Enabled reliable AI-driven querying and analytics across heterogeneous domains. Built reusable MCP infrastructure to support structured, tool-based AI reasoning.

SYS.03

HackClub Recruitment Portal

Next.jsTypeScriptSupabasePostgreSQLRole-Based Access ControlTailwind CSSVercel

PROBLEM_SPACE

Manual recruitment workflows do not scale efficiently and lead to inconsistent evaluation, processing delays, and administrative overhead.

ARCHITECTURE

Led end-to-end development of a production recruitment platform handling 800–1000+ applicants per cycle. Designed role-based access control, status tracking workflows, interview scheduling pipelines, and recruiter analytics dashboards. Architected the system using Supabase (PostgreSQL, Auth, RLS) with Next.js and deployed via Vercel.

IMPACT_METRICS

Reduced recruitment processing time by approximately 70% while maintaining 99.9% uptime. Improved evaluation consistency and decision transparency across recruitment cycles.

[MOD.04] // MATRIX

CAPABILITIES

CAP.01

SYSTEMS_&_CORE

  • Operating Systems
  • Memory Management
  • File Systems
  • Data Structures & Algorithms
  • Computer Networks
  • System Design
  • Performance Optimization
  • Low-Level Programming
CAP.02

BACKEND_&_DATA

  • Node.js
  • Express.js
  • REST API Design
  • PostgreSQL
  • MySQL
  • SQL
  • Supabase (Auth, RLS)
  • MongoDB
  • Role-Based Access Control (RBAC)
  • Backend State Management
CAP.03

AI_&_INTELLIGENT_SYSTEMS

  • Retrieval-Augmented Generation (RAG)
  • Hallucination Analysis
  • LLM Integration (API-based)
  • Model Context Protocol (MCP)
  • Tool-based AI Systems
  • Prompt Engineering
  • AI Evaluation Pipelines
  • NLP Evaluation
CAP.04

ML_&_DATA_SCIENCE

  • Python
  • Machine Learning Fundamentals
  • Regression & Classification
  • CNN (Foundational)
  • Data Preprocessing
  • Applied ML Experiments
  • R Programming
CAP.05

FRONTEND_ARCH

  • Next.js (App Router)
  • React
  • TypeScript
  • JavaScript
  • Tailwind CSS
  • Accessibility (WCAG)
  • Component Architecture
CAP.06

INFRA_&_TOOLS

  • Git & GitHub
  • Linux
  • Docker (Foundational)
  • System Design
  • Performance Profiling
  • Production Debugging
  • AI Integration Tooling
CAP.07

Programming LANGUAGES

  • C
  • C++
  • Python
  • Java
  • JavaScript
  • TypeScript
  • R
  • Rust
[MOD.05] // HISTORY

EXECUTION_LOG

LOG.01HACK_CLUB

WEB_DEVELOPMENT_LEAD

Directed technical initiatives and mentored team members on architecture decisions. Shipped production-ready web projects, enforcing code quality and scalable design patterns.

LOG.02VARIOUS

HACKATHON_&_ACADEMIC_ENG

Consistently delivered end-to-end systems under strict time constraints. Focused on architectural correctness, database normalization, and system design over superficial UI polish.

LOG.03GITHUB

OPEN_SOURCE_CONTRIBUTOR

Maintainer of publicly documented repositories emphasizing clean commit histories, readable code, and comprehensive technical documentation.

[SYS.TERM] // END

COLOPHON

SYSTEM_HALTED

Engineered by Divik Dhiman. Rendered via Next.js & Tailwind CSS. Typography: Rajdhani, IBM Plex Sans, Geist Mono.