Enterprise Cloud · DevOps · AI/ML · Email · Marketing Expert
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Enterprise Cloud · DevOps · AI/ML · Email · Marketing Expert
Fast CV Writing, MATLAB & VOIP Services by Pro
Fast CV Writing, MATLAB & VOIP Services by Pro
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Senior Full-Stack Engineer
🚀 Currently optimizing my portfolio site for search intent & technical SEO.
Most developers treat their portfolio as a static resume. I’m treating mine like a high-performance product that works around the clock.
Here is the exact technical and strategic breakdown of what I’m tackling right now:
1. Core Web Vitals & Technical Hygiene
Pushing for 95+ PageSpeed scores across mobile and desktop.
Eliminating render-blocking scripts, tuning image pipelines (WebP/AVIF compression with explicit aspect ratios to kill CLS), and caching assets aggressively.
Auditing DOM size and clean server response times ($TTFB$).
2. Architecture & Structured Data
Implementing comprehensive JSON-LD schemas (Person, WebSite, and targeted CreativeWork schemas on technical case studies).
Generating a clean, dynamic XML sitemap and optimizing canonical tags to prevent duplicate indexing across custom deployments.
3. Search Intent & Keyword Architecture
Moving away from generic "Full-Stack Developer" titles to intent-driven queries: highlighting specific problems I solve (e.g., custom API architectures, high-load web systems, performance refactoring).
Structuring project deep-dives around searchable problem-solution frameworks rather than just bullet lists of tech stacks.
4. OpenGraph & Shareability
Dynamic OG cards for case studies so technical breakdowns look sharp when shared across social channels and communities.
A developer’s portfolio should demonstrate clean architecture before a client even reads a line of code.
What’s the single biggest needle-mover you’ve seen when optimizing your personal site for inbound search? Let’s swap notes in the comments. 👇
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AI Automation & Full-Stack Developer | Computer Vision • NLP
New to Contra
AI Automation & Full-Stack Developer | Computer Vision • NLP
VisiHealth AI – Explainable Medical VQA
VisiHealth AI is a multimodal, explainable Medical Visual Question Answering system designed to analyze complex clinical radiology scans. Utilizing PyTorch, ResNet-50, and BioLinkBERT, the platform fuses text and spatial image patches through a real multi-token cross-attention mechanism to provide accurate clinical insights. It builds diagnostic trust through explainable AI by overlaying attention-map heatmaps on original scans and deriving human-readable rationales from a 4,444-triplet medical knowledge graph. Delivered via a scalable Next.js and Flask architecture, it demonstrates my capability in deep learning and healthcare AI integrations
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NexusAI — Enterprise Agentic Assistant
NexusAI is a production-ready enterprise agentic conversational platform that revolutionizes e-commerce through intelligent intent classification and semantic product search. Built with LangGraph, FastAPI, and Next.js 15, this AI assistant utilizes advanced RAG pipelines and Supabase pgvector to deliver highly personalized, context-aware recommendations in real-time. By leveraging Groq's LPU for sub-second LLM inference and FlashRank for cross-encoder analysis, the system ensures unparalleled accuracy and speed. This project highlights my advanced proficiency in LLM orchestration, vector databases, and real-time frontend integration via Server-Sent Events.
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Smart Farming Advisor — Agentic AI
The Smart Farming Advisor is an end-to-end agentic AI system that empowers the agriculture sector with data-driven crop recommendations and accurate plant disease detection. Utilizing an intelligent agentic routing system for autonomous intent classification, the platform seamlessly integrates computer vision models (ResNet50) and machine learning classifiers (Random Forest). It features a comprehensive RAG pipeline with semantic search for farming Q&A, bridging a Next.js 14 frontend with a Flask REST API backend. This project emphasizes my ability to orchestrate multi-tool AI assistants and deliver high-impact predictive modeling solutions.
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Full stack software engineer empowering businesses
Full stack software engineer empowering businesses
Custom system & IOS/Android App developer
An affiliate partner system created with 8 product sales pages tracking back conversions to the partner portal where the partners can earn their commission from promoting the products
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Software Developer
Designed and developed Well Lift, an Android fitness app for tracking calories, workouts, and weight. It features a guided onboarding flow, a daily nutrition dashboard with macro charts, an exercise library with calorie-burn calculation, a calendar for workout planning, and progress statistics, all wrapped in a clean, user-friendly interface.
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Network Observer: Air-Gapped Enterprise LAN Telemetry & Topology Engine
Executive Summary
Network Observer is a self-hosted, sovereign network telemetry and topology visualization suite engineered for air-gapped university campus labs and secure enterprise subnets. Designed from the ground up for zero external cloud dependencies, it delivers real-time device health monitoring, automated subnet discovery, hierarchical computer lab grouping, and interactive DAG network graphs with an ultra-lean memory footprint (< 500 MB RAM).
The Challenge
Educational institutions and secure facilities manage hundreds of lab machines, routers, and managed switches distributed across segmented VLANs. Existing solutions were either:
Cloud-Tethered SaaS: Violating data residency constraints and failing entirely in air-gapped environments.
Resource-Heavy Legacy Tools: Bloated platforms consuming tens of gigabytes of RAM with outdated, clunky interfaces.
Disconnected Tooling: Requiring separate software for ping sweeps, topology mapping, and asset inventory.
The goal was to build a modern, unified, tactical NOC command center that operates entirely offline, boots in seconds, and handles live telemetry without straining hardware.
Engineering & Architectural Philosophy
As the Lead Architect, I directed the end-to-end technical requirements, database schemas, API contracts, and UX patterns, leveraging state-of-the-art AI pair-programming workflows to rapidly execute and harden production code across the full stack.
Strict 4-Tier Clean Architecture: Domain, Application, Infrastructure, and Presentation layers ensuring complete testability and separation of concerns.
Zero Raw SQL Overhead: Strongly-typed EF Core 10 LINQ queries coupled with a relational PostgreSQL 18 backend running sub-millisecond query execution.
Deterministic Resource Budgeting: Engineered with an explicit ceiling under 500 MB RAM across both Kestrel backend and the frontend runtime.
Comprehensive Automated Verification: 151 automated integration and unit tests covering domain invariant rules, ICMP probes, and RBAC authentication.
Key Features & Innovations
1. Interactive Cytoscape.js Topology Canvas
Real-time network mapping with dynamic layout toggling: Lab Hierarchies (Core Router → Aggregate Switch → Computer Lab Groups → Workstations) or Pure Device-to-Device.
High-performance SVG nodes styled with tactical cyber-optic color palettes, transparent glyph backgrounds, and auto-wrapping status badges.
Hardware-accelerated cursor-anchored smooth zooming and collision-avoidance Dagre layouting.
2. Live Telemetry & Slide-Over Inspector Drawer
Background non-blocking worker threads conducting periodic ICMP/SNMP telemetry sweeps every 5 seconds.
Instant slide-over inspection drawer displaying live CPU/RAM utilization metrics, uptime gauges, IP/MAC bindings, and vendor footprints.
Route-aware state management that collapses drawers seamlessly upon switching views.
3. Subnet Sweep Discovery & Staging Pipeline
Parallelized CIDR sweep engine (e.g., 192.168.1.0/24) capable of identifying active hosts and resolving MAC/vendor footprints in seconds.
One-click staging pipeline allowing discovered network hosts to be audited and promoted directly into production inventory.
4. Role-Based Governance & Audit Trail
Air-gapped JWT token authentication paired with cryptographically secure PBKDF2 passphrase hashing.
Immutable append-only audit trail logging every device provisioning, IP reassignment, and status mutation.
Technology Stack
Backend: C# .NET 10 Kestrel Web API, Entity Framework Core 10
Database: PostgreSQL 18 with relational integrity constraints & indexing
Frontend: Vue 3 (Composition API), TypeScript, Vite, Pinia Store
Canvas Visualization: Cytoscape.js with Dagre Hierarchical Layout Engine
Styling: Tailwind CSS, Lucide Icons, Custom Tactical NOC Theme
DevOps & Quality: GitHub Actions CI/CD Pipeline, xUnit, FluentAssertions (151 passing tests)
Key Metrics & Results
100% Air-Gapped Sovereign: Zero external telemetry pings or telemetry leakage.
< 500 MB Total Memory Ceiling: Runs seamlessly on resource-constrained campus edge servers.
151/151 Test Coverage: Zero test regressions with automated GitHub Actions verification.
Sub-Millisecond Queries: Fast relational indexing across hundreds of concurrent lab nodes.
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About a year ago, I published my Python package, collapsiblepane, on PyPI.
Today, it has crossed 7,000+ downloads!
It's a great feeling to see a small project I built reach other developers and get used over time. Moments like these remind me that even a small contribution can have an impact when you put your work out there.
Thank you to everyone who has installed or tried the package. I truly appreciate it!
Looking forward to building and sharing more useful tools with the developer community.
Check out the package: https://pypi.org/project/collapsiblepane/
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I designed and built this app end to end as a native Android product. It’s offline-first, so every feature works without a connection, and the balance always stays accurate even after edits and deletions. Users can log income and expenses, organize them with default and custom categories, and find anything quickly with date-range filters and search. Behind the clean UI is a tested architecture (Kotlin, Jetpack Compose, Room, Hilt, MVVM), with unit and instrumented tests covering the data layer and view models.
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Production Grade AI Systems
FAWAN OS Neural 1: Enterprise Autonomous Swarm Architecture
I engineered an enterprise-grade, multi-agent AI infrastructure designed to completely automate mission-critical workflows, eliminate data silos, and guarantee 100% uptime with self-healing capabilities.
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🔧 Fix, Harden & Scale Broken AI Workflows (Zero Timeouts | Bulletproof Architecture)
Most automation pipelines break the moment production traffic spikes. Brittle webhooks fail, API rate limits crash running flows, and unhandled LLM hallucinations cause silent data corruption across CRMs and databases.
If your team is losing dozens of hours manually auditing failed runs in n8n, Make, or Zapier, you don't need another patchwork fix—you need hardened systems engineering:
Fault-Tolerant Error Handling: Implement deterministic retries, dead-letter queues, and fallbacks so no webhook or payload is ever lost.
Orchestration Upgrades: Refactor fragile linear chains into stateful, modular multi-agent architectures using LangGraph & CrewAI.
Zero-Timeout Infrastructure: Optimize API execution paths, batch requests, and sanitize inputs/outputs to handle high throughput smoothly.
Full Done-For-You Delivery: Save 1,000+ hours/month by converting chaotic manual backend routines into self-healing, automated pipelines.
Stop letting broken runs stall your operations.
Have an existing workflow that keeps timing out, or need a robust custom agent pipeline built from scratch? Send me an inquiry or hire me directly through my profile.
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🛡️ Engineering Secure, Enterprise-Grade AI Architectures
Deploying AI into real business operations requires more than just connecting LLMs to basic APIs. Without strict guardrails, automated agents can introduce serious security vulnerabilities, data leaks, and unpredictable system failures.
When building mission-critical AI systems, I prioritize zero-trust infrastructure and deterministic execution:
Autonomous Multi-Agent Swarms: Orchestrated, role-specific agents designed to collaborate with state persistence and self-healing error handling.
Zero-Trust Security Pipelines: Role-based access controls, sanitized input/output validation, and credential isolation to safeguard proprietary data.
Scalable Enterprise Automation: Built for resilient high-throughput operations, eliminating manual bottlenecks across APIs, CRMs, and internal databases.
The future of automation belongs to systems that are not just smart, but verifiable, auditable, and secure.
Are you preparing to deploy autonomous AI agents or integrate robust automation pipelines into your enterprise stack? Send me an inquiry or hire me directly through my Contra profile.
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