📍 Abu Dhabi, UAE • 📧 [email protected]
Building production AI systems that process 200k+ requests/min. Previously scaled distributed systems serving millions. Open source contributor with 4k+ GitHub stars.
| System | Scale | Achievement |
|---|---|---|
| Fintech Document AI | 200k docs/min | 99.5% accuracy, $0.01/doc (90% cost reduction) |
| Portrait Generation Pipeline | 10k+ cards/batch | 95% cost reduction vs commercial APIs |
| MCP Server Platform | O(log n) complexity | 33% infrastructure savings, 57% compute optimization |
| Database Optimization | 45s → 200ms queries | 225x performance improvement |
| System Reliability | 88% → 99.9% uptime | $2M+ saved in downtime costs |
AI/ML: OpenAI GPT-4, Anthropic Claude, Llama 3.1 • Stable Diffusion XL, InstantID • LangChain, PyTorch, Hugging Face • RAG systems, agentic workflows, vector search
Backend: Java, Python, Go, TypeScript • PostgreSQL, Redis, DynamoDB, Elasticsearch • Kafka, AWS SQS, EventBridge
Cloud: AWS (Bedrock, SageMaker, ECS/EKS, CDK) • Kubernetes, Terraform • NewRelic, DataDog, Prometheus
Agentic Compliance Engine
AI agents that understand regulatory nuance and auto-generate audit trails. Multi-modal validation pipeline with 98% audit completeness, reducing review time from 1-3 weeks to 2-4 hours.
DiffusionID Production Pipeline
Identity-preserving portrait generation at scale. 8 seconds/image on RTX 4090, 450 images/hour throughput. Two-stage pipeline with automated PDF card generation.
FAANG Interview Questions
3.7k+ stars. Comprehensive coding interview prep resource.
Screwdriver CI/CD
10+ merged PRs across UI, models, and core platform. Added GitHub/GitLab PR validations for 10k+ engineers.
TranscriptAI
Multilingual transcription using OpenAI Whisper.
def architect_ai_system(business_requirements):
"""
Lessons from shipping AI to production:
1. Start with the data pipeline (garbage in, garbage out)
2. Vector databases are the new relational databases
3. Prompt engineering is systematic, not magic
4. Always have a fallback to traditional algorithms
5. Monitor for drift, hallucinations, and edge cases
"""
if requires_compliance():
return multi_layer_validation() + audit_trails()
elif requires_scale():
return async_processing() + caching_layers()
return simple_and_maintainable()Researching multi-agent orchestration and vector search optimization. Mentoring engineers on distributed systems and AI system design. Writing technical content reaching 80k+ engineers and founders. Giving consultations to AI teams for building better solutions.