I'm a full-stack developer with 7+ years of experience building intelligent, scalable applications that solve real-world problems. My work sits at the intersection of AI/ML and modern web/mobile development, where I transform complex machine learning models into intuitive user experiences.

Throughout my career, I've helped businesses leverage AI to drive measurable results - from recommendation engines that increased user engagement by 26% to computer vision systems processing thousands of images per second. I specialize in taking AI from concept to production, ensuring models are not just accurate but also fast, reliable, and integrated seamlessly into applications users love.

I'm passionate about clean code, mentoring teams, and staying at the forefront of technology. Whether it's integrating GPT-4 into a Laravel application, deploying PyTorch models on mobile devices, or architecting microservices that scale, I bring both technical depth and a pragmatic, user-first approach.

My primary focus is building production-ready AI systems that deliver real business value.

• Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
• Deep Learning: CNN, RNN, LSTM, Transformer architectures (BERT, GPT, T5)
• Computer Vision: OpenCV, YOLO, MediaPipe for real-time object detection and image processing
• NLP: LangChain, Hugging Face Transformers, spaCy for natural language understanding
• MLOps: MLflow, Kubeflow, Weights & Biases for model versioning and monitoring
• AI APIs: OpenAI, Anthropic, Google AI, Azure Cognitive Services

I build end-to-end solutions across web and mobile platforms.

Frontend: React, Next.js, Vue.js, Angular | React Native, Flutter for mobile | Tailwind CSS, Styled Components

Backend: Laravel (PHP), Django, FastAPI (Python), Node.js (Express, NestJS) | REST, GraphQL, WebSocket APIs

Databases: PostgreSQL, MySQL, MongoDB, Redis, Vector DBs (Pinecone, Weaviate)

DevOps: Docker, Kubernetes, GitHub Actions, AWS, Google Cloud, Azure

Built a production-grade conversational AI system using GPT-4 with custom fine-tuning and RAG (Retrieval-Augmented Generation) to ground responses in domain-specific knowledge. The system handles thousands of concurrent users with <200ms response times and includes comprehensive context management and conversation history.

Tech: GPT-4, LangChain, Vector DB (Pinecone), FastAPI, React

Designed and deployed a personalized recommendation system achieving 95% accuracy that increased user engagement by 26% and boosted average order value by 18%. Built with collaborative filtering and deep learning models, processing millions of user interactions daily.

Tech: PyTorch, Redis, PostgreSQL, Laravel API, React frontend

Developed a scalable object detection and classification system processing 50+ video streams simultaneously. Used in production for inventory management and quality control, reducing manual inspection time by 70%.

Tech: YOLO, OpenCV, TensorFlow, Kubernetes, WebSocket streaming

Created a cross-platform fitness app with computer vision-powered form analysis and personalized workout recommendations. On-device ML inference ensures privacy while delivering real-time feedback to users.

Tech: React Native, TensorFlow Lite, PyTorch Mobile, Firebase

Built a custom customer relationship management system with automated lead scoring using machine learning. The predictive model analyzes behavioral patterns to prioritize high-value prospects, increasing sales team efficiency by 40%.

Tech: Laravel, Python (Scikit-learn), MySQL, Vue.js

Architected an automated sentiment analysis and trend detection pipeline processing 100K+ social media posts daily. Provides real-time alerts and actionable insights for brand reputation management.

Tech: BERT, spaCy, Kafka, Django, PostgreSQL, React dashboard

I believe the best AI solutions combine solid engineering fundamentals with cutting-edge ML techniques. Here are examples of my work:

Custom Transformer Fine-Tuning (PyTorch)

import torch.nn as nn
from transformers import AutoModel

class CustomBERTClassifier(nn.Module):
    def __init__(self, model_name, num_classes):
        super().__init__()
        self.bert = AutoModel.from_pretrained(model_name)
        self.classifier = nn.Linear(768, num_classes)
    
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids, attention_mask=attention_mask)
        return self.classifier(outputs.pooler_output)

AI-Powered Laravel API

class AIController extends Controller
{
    public function generateContent(Request $request)
    {
        $response = OpenAI::chat()->create([
            'model' => 'gpt-4',
            'messages' => [
                ['role' => 'system', 'content' => 'You are a helpful assistant.'],
                ['role' => 'user', 'content' => $request->input('prompt')]
            ],
            'temperature' => 0.7,
        ]);
        
        return response()->json([
            'content' => $response->choices[0]->message->content
        ]);
    }
}

React Streaming Chat Interface

import { useChat } from 'ai/react';

export function ChatInterface() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat();
  
  return (
    <div className="chat-container">
      {messages.map(m => (
        <div key={m.id} className={`message ${m.role}`}>
          {m.content}
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input 
          value={input} 
          onChange={handleInputChange}
          disabled={isLoading}
          placeholder="Ask me anything..."
        />
      </form>
    </div>
  );
}

Over my career, I've had the privilege of working on projects that deliver tangible results:

• 20+ AI-powered applications serving 100K+ users with 99.9% uptime
• ML models achieving 95%+ accuracy across computer vision, NLP, and recommendation systems
• Reduced inference latency by 60% through model optimization and efficient deployment strategies
• Increased business metrics including 26% engagement boost and 40% sales efficiency improvement
• Contributed to open-source AI libraries and tools with 1K+ combined GitHub stars
• Mentored development teams on best practices for AI integration and MLOps

Beyond technical skills, I focus on understanding the business problem first, then choosing the right AI approach - whether that's a simple heuristic, a pre-trained model, or a custom deep learning solution. I'm experienced in:

• Rapid Prototyping: From concept to working demo in days, not weeks
• Production Deployment: Building systems that scale, monitor themselves, and gracefully handle edge cases
• Cross-functional Collaboration: Translating between technical teams, stakeholders, and end users
• Continuous Learning: Staying current with the latest research and quickly adapting new techniques

• TensorFlow Developer Certificate (Google)
• AWS Machine Learning Specialty
• Microsoft Azure AI Engineer Associate
• Deep Learning Specialization (DeepLearning.AI)
• Laravel Certified Developer

I'm always interested in challenging projects, especially those that push the boundaries of what's possible with AI. Whether you're looking to build something new or optimize existing systems, I'd love to discuss how we can work together.

Open to: Full-time roles, contract work, technical consulting, and collaborative projects

"The best AI solutions are invisible - they just make things work better."