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.