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Full-Stack & AI Systems Engineering•Raja Irfan Ahmed

I turn complex engineering challenges into high-performance web products.

Full-stack development with thoughtful user experiences and practical, production-minded AI integration.

Explore Selected WorkInspect System Architecture
Live Production Architecture Pattern
Click a pipeline node to inspect implementation decisions
Next.js Edge Runtime Detail:Zero cold-start edge middleware validating JWT auth and rate limits before routing to serverless inference enclaves.
Production Standard
Selected Work (03 Case Studies)

Production Systems & Case Studies

Detailed technical breakdowns covering problem context, personal ownership, architecture decisions, and measured trade-offs.

Filter Scope:
AI Engineering & Enterprise Search2025-2026

Enterprise Hybrid RAG & Vector Knowledge Engine

A production-grade semantic retrieval engine processing over 250,000 technical engineering manuals with sub-200ms answer latency.

Retrieval Latency
< 180ms p95
Precision Score
94.6% Top-3
Document Scale
250K+ Manuals
Concrete Challenge

Engineers were losing hours searching legacy PDF manuals. Standard cosine vector retrieval hallucinated frequently on exact alphanumeric part numbers and schematic codes.

System Architecture

Built a multi-stage hybrid retrieval pipeline combining reciprocal rank fusion over dense OpenAI text embeddings and PostgreSQL pgvector with BM25 full-text keyword indexing.

Key Engineering Trade-off & Rationale

Selected PostgreSQL pgvector over standalone vector databases like Pinecone to eliminate cross-network latency and keep sensitive enterprise data within the unified ACID compliance boundary.

Next.js App RouterTypeScriptPythonFastAPIPostgreSQLpgvectorDocker
Inspect Code on GitHub
Distributed Systems & Frontend Craft2025

Real-Time Collaborative Canvas with CRDT State Synchronization

A high-performance interactive visual canvas enabling simultaneous multi-cursor design collaboration with zero server-side lock contention.

Sync Latency
< 12ms Local
Concurrent Users
64 per Room
Frame Rate
120 FPS Locked
Fintech & High-Volume Data Systems2024-2025

High-Throughput Financial Telemetry & Streaming Analytics

Real-time market analytics dashboard decoding 40,000 ticker events per second into interactive charts without main-thread UI stutter.

Throughput
40K ticks/sec
Main Thread Jitter
0ms Drops
Bandwidth Reduction
68% Binary
Engineering Blueprints

Architectural Principles for AI-Augmented Web Systems

How I architect resilient, zero-bloat web systems capable of serving streaming inference at scale with verifiable correctness and strict latency budgets.

01. Edge Authentication & Ingress
TypeScript / SQL Implementation
Production Guarantee:

Sub-15ms worldwide response time with zero database round-trips for invalid sessions.

SNIPPET: EDGE.TS
// Edge Auth Middleware Pattern
export async function middleware(req: NextRequest) {
  const sessionToken = req.cookies.get("session")?.value;
  if (!sessionToken) {
    return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
  }
  const { allowed, remaining } = await rateLimiter.limit(sessionToken);
  if (!allowed) {
    return new NextResponse("Too Many Requests", { status: 429 });
  }
  return NextResponse.next();
}
Pattern: Edge Middleware + Distributed Redis Token BucketZero Fluff Architecture
Capabilities Matrix

Core Technical Capabilities

Organized by real architectural use cases and demonstrated production utility rather than isolated logo walls.

AI & Vector Systems

Production inference pipelines, embeddings, and context retrieval

Hybrid RAG PipelinesReciprocal rank fusion with pgvector & BM25
LLM Streaming & SSEBackpressure-safe tokenizers and AST chunking
Model IntegrationAnthropic Claude, OpenAI, and local Ollama runtimes
Structured OutputsZod-validated tool calls and JSON schema enforcers

Frontend & Design Engineering

Tactile, responsive, zero-bloat user interfaces

Next.js App RouterReact Server Components, Suspense, streaming SSR
TypeScript StrictZero-any type safety, discriminating unions
Tailwind CSS v4Fluid clamp typography, container queries, CSS variables
Fluid Motion & Web WorkersEmil Kowalski easing physics, off-thread compute

Backend & Distributed Data

High-throughput APIs, data persistence, and state sync

PostgreSQL & pgvectorACID transactions, relational indexing, HNSW vector search
Python & FastAPIAsync microservices, Pydantic validation, scientific libraries
Node.js & Edge RuntimesLow-latency edge route handlers, WebStreams
WebSockets & CRDTsReal-time state synchronization, conflict resolution

Infrastructure & Quality

Deterministic builds, deployment velocity, and monitoring

Docker ContainerizationMulti-stage production builds, minimal surface images
CI/CD & GitHub ActionsAutomated linting, type-check, and preview enclaves
Web Performance & Core VitalsSub-100ms TTFB, 0 CLS, 100/100 Lighthouse audits
WCAG AA AccessibilityKeyboard navigation, screen reader live regions, high contrast
Working Philosophy & Approach

Practical Engineering Beats Dogmatic Hype

I approach software engineering with a bias toward simplicity, verifiable reliability, and human ergonomics. AI is not magic dust to sprinkle haphazardly over every interaction; it is a powerful computational primitive that demands defensive architectures, strict schema contracts, and graceful deterministic fallbacks.

Whether building an enterprise retrieval pipeline or choreographing micro-interactions on the frontend, my priority is clear: deliver fast, resilient systems that real users and engineering teams can rely on with zero surprises.