Is Mohd Sakib available for full-time employment or freelance contracts?

Yes — I am currently open to senior full-time roles and high-stakes freelance contracts. I'm based in Meerut, India and work remotely across time zones. Reach out at mohdsakib.work@gmail.com to discuss opportunities.

What tech stack does Mohd Sakib specialize in?

My core stack is Next.js, Node.js, React Native, TypeScript, and PostgreSQL. I have production experience with Redis (pub/sub, caching), WebSockets, GraphQL, Kafka, Docker, AWS (EC2, ECS, EKS, S3), Python/FastAPI, and AI/LLM systems including RAG pipelines with LangChain, Pinecone, and pgvector. I choose tools based on the problem, not preference.

What kind of products has Mohd Sakib shipped in production?

I've architected and shipped 8 production-grade products: FinTech (Sendora — cross-border remittances, PCI-DSS compliant, $50K+ processed), EdTech (Pademi — 25K+ students, WebRTC live classrooms), Gaming (StakeClash — real-time wagering, $12K MRR, 200+ concurrent users), LegalTech (CoLaw — 200+ law firms, $15K+ MRR, 100GB+ daily data), E-Commerce (Tekish — $100K+ GMV/month, 50K+ monthly shoppers), and AI (Artificial Mufti — RAG system with 45% hallucination reduction, 200+ DAU).

Can Mohd Sakib lead technical architecture and system design decisions?

Yes. Across every project I've owned full system design decisions — from stateless WebSocket architectures with Redis pub/sub for horizontal scaling, to Kafka event-sourced microservices for FinTech compliance, to multi-stage RAG pipeline design with vector reranking. I've set up containerized Kubernetes deployments, automated CI/CD pipelines reducing deployment friction by 70%, and made explicit technology-tradeoff decisions documented in each case study.

What performance standards does Mohd Sakib build to?

I treat performance as a business requirement, not an afterthought. My portfolio itself scores 98 on Lighthouse with LCP 1.2s, INP 38ms, and CLS 0.01. Across client work: improved LCP by 2.1s on Tekish (boosting conversions 22%), achieved sub-72ms p99 socket latency on StakeClash, reduced React Native bundle size by 88%, maintained 60fps rendering on Sendora's fintech flows, and cut INP by 90% through React concurrency and dynamic code splitting.

8 products · 25K+ users · $100K+ GMV · 98 Lighthouse

I build software that
ships, scales, and earns.

View Case Studies →Let's Talk →

25K+Users Served

$100K+GMV Shipped

98Lighthouse Score

8Production Systems

Selected Work

Products I've Built.

↗

client

StakeClash

Real-time competitive gaming and decentralized wagering ecosystem

1K+ MAU$12K MRR99 Lighthouse200+ concurrent

Web App ↗Visit Website ↗

startup

Artificial Mufti

AI-powered comprehensive Islamic knowledge and guidance platform

500+ DOWNLOADS$2K MRR98 Lighthouse200+ Daily Active Users

Web App ↗Visit Website ↗

client

Pademi

Interactive EdTech and Social Learning Mobile Platform

25K+ StudentsEnterprise B2BHighly OptimizedNationwide Deployment

FULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEERFULL-STACK ENGINEER

Archive & More

SEE ALL
WORK.

Dive deep into all my enterprise, startup, and full-stack engineering side projects.

Zero Tolerance
For Latency.

I don’t merely build accessible UIs — I instrument, measure, and surgically optimize every millisecond of the execution thread. Real scores. Real impact.

Lighthouse Performance

Average auditing score across production environments, factoring in edge caching and asset optimization.

Core Web Vitals

LCPTarget: <2.5s

1.2s

1s delay in page load = 7% drop in conversions. At 1.2s, checkout flows stay frictionless.

CLSTarget: <0.1

0.01

Zero layout shift during load means users never misclick. Critical for e-commerce CTAs and form inputs.

INPTarget: <200ms

38ms

Inputs feel instant. At 38ms users perceive zero lag — the threshold for 'feels native'.

FCPTarget: <1.8s

0.6s

Content visible in 0.6s keeps bounce rate low. Users see something before they can even lift their finger.

Before & After Optimization Letency

Largest Contentful Paint

4.8s

→75% better

1.2s

Interaction to Next Paint

380ms

→90% better

38ms

Total Bundle Size

1.8MB

→88% better

210KB

Optimization Architecture

React Concurrency

Interruptible rendering to eliminate input jank and main-thread blocking.

Dynamic Code Splitting

Component-level granular chunking. Shipping precisely zero unnecessary bytes.

Edge Architecture

ISR and CDN edge rules creating sub-10ms Time To First Byte globally.

Memory Allocation

Aggressive cleanup sweeps and strict object pooling to defeat memory leaks.

Asset Pipeline

AVIF/WebP transpilation, lazy hydration, and hardcoded aspect ratios for zero CLS.

Custom WebGL/GSAP

Offloading calculations to GPU threads avoiding standard DOM reflows.

Capabilities

AI Systems &
Orchestration

Building AI-native products — from retrieval pipelines and fine-tuned models to real-time inference and zero-touch business operations.

01 // ARCHITECTURE

Automated Outbound Pipeline

Zero-touch lead generation. Handles automated scraping, deduplication against CRM state, API enrichment via Hunter.io, and drafts hyper-personalized outreach.

n8nLinkedIn APIGeminiHunter.io

02 // ARCHITECTURE

Stateful Agentic Follow-ups

CRON-triggered autonomous agents that read database states to identify pending actions, generating contextual sequences based on historical interaction data.

State ManagementCRONContext Windowing

03 // ARCHITECTURE

Intelligent Triage Engine

Real-time inbox monitoring system utilizing LLMs to classify intent, filter spam, and dynamically route queries. Features human-in-the-loop escalation.

WebhooksClassificationTelegram API

04 // ARCHITECTURE

Autonomous Reporting

Scheduled data aggregation pipelines that compile weekly performance metrics, format statistical reports, and dispatch them to secure administrative channels.

Data AggregationAnalyticsPipeline Arch

Core Capabilities

LLM Integration

Production pipelines with streaming, tool-calling, and multi-agent orchestration.

OpenAIClaudeGeminiVercel AI SDK

RAG Systems

Retrieval pipelines with semantic chunking, re-ranking, and hybrid search.

LangChainpgvectorSupabasePinecone

Computer Vision

Real-time detection, pose estimation, OCR — deployed to edge for ultra-low latency.

PyTorchYOLOMediaPipeONNX

Fine-Tuning

LoRA / QLoRA tuning on custom datasets with efficient GPU utilization.

HuggingFacePEFTModalAxolotl

Live Architecture

38-Node Production Pipeline.

One autonomous system, four distinct layers. The same workflow powering Krapton's client acquisition — explore each phase below.

Phase 01 — Outbound Pipeline

Zero-touch lead generation. Scrapes LinkedIn, deduplicates against live CRM state, enriches via Hunter.io, and drafts hyper-personalized outreach via Gemini — 10 qualified leads per day, no human input.

LinkedIn APIHunter.ioGemini 2.5Google Sheets

10 leads / day

drag · zoom ±

CS

From algorithms to distributed systems — trained daily, applied in production.

50+ Solved20+ Streak80% AccuracyTop 10%

Easy: 35+ / Medium: 20+ / Hard: 10+

Sliding Window

O(n) over O(n²)

Two Pointers

Linear scan optimizations

DFS / BFS

Graph traversal backbone

Dynamic Programming

State optimization

Binary Search

Search on answer space

Backtracking

Combinatorial pruning

Union Find

Near O(1) connectivity

System Design

Scalability

Sharding, CQRS, consistent hashing

Caching

Redis, CDN, write strategies

Messaging

Kafka, queues, API patterns

Reliability

Retries, circuit breakers

Systems I Can Design

Twitter / X — fanout, timeline

YouTube — pipeline, streaming

E-commerce — inventory, payments

Realtime Chat — WebSockets

URL Shortener — high QPS

Search Engine — indexing

About

Engineer by trade.
Architect by instinct.

I'm Mohd Sakib, a Senior Full Stack Developer and React Native specialist based in Meerut, Uttar Pradesh. Over the past 3+ years I've architected and shipped 8 production-grade products across gaming, FinTech, EdTech, LegalTech, and AI — serving 25,000+ users and processing $100K+ in transactions.

My core stack is Next.js, Node.js, React Native, and PostgreSQL — but I choose tools based on the problem, not preference. I've built stateless WebSocket architectures with Redis pub/sub for sub-72ms real-time systems, designed Kafka event-sourced microservices for PCI-DSS compliance, and engineered two-stage RAG pipelines that cut hallucination rates by 45%.

Currently available for senior full-time roles or high-stakes freelance contracts where performance and architecture quality are non-negotiable.

Get In Touch →LinkedIn ↗

Career Timeline

2021

Wrote first production line of code. Shipped a client web app within 3 months.

2022

First enterprise client. Delivered a nationwide EdTech platform (Pademi) to 25K+ students.

2023

Expanded into AI systems. Built RAG pipelines, LLM integrations, and cross-border FinTech apps.

2024

8 live production systems. $100K+ GMV processed. 25K+ users served. 98 Lighthouse.

Core Stack

Next.jsNode.jsReact NativeTypeScriptPostgreSQLRedisAWSDockerPython / FastAPIGraphQLKafkaKubernetes

Location

Meerut, India

Experience

3+ Years

Availability

Immediate

Work Mode

Remote / Hybrid

I build software thatships, scales, and earns.

Products I've Built.

StakeClash

Artificial Mufti

Pademi

Sendora

CoLaw

Tekish

Teppe

Techs

StakeClash

Artificial Mufti

Pademi

SEE ALLWORK.