🚀 Solutions That Drive ROI
🌟 Industry Recognition
🏆 AWS ML/AI Certified • 🚀 40+ Projects • 📝 30+ Technical Articles • 📚 100K+ Readers Reached • 👨🏫 Teacher
Personal Platforms
Professional Platforms
Technical content, consulting, and partnerships
| For Technical Leaders | For Content Needs | For Partnerships |
|---|---|---|
| Development, Strategy | Tutorials, Documentation, Guides | Teaching, Collaboration, Consulting |
I specialize in teaching, technical writing, and developing practical, scalable systems that combine deep technical expertise with tangible business impact, ranging from LLM-powered applications to full-stack ML solutions.
Additionally, I am actively engaged with the global tech community as an AWS Community Builder, Qdrant Star, and Docker Captain. Through these roles, I share my expertise, contribute to open collaboration, and help foster innovation across the cloud and developer ecosystems.
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Programming Languages
Machine Learning & Data Science
LLMs & AI
Vector Databases
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UI & REST APIs
MLOps & Engineering
DevOps Best Practices
Cloud & Infrastructure
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My technical writing has been featured in major industry newsletters and platforms, such as LlamaIndex Newsletter, GKE Newsletter, and the MLOps community:
- 📝 Automate Vector Database Update with AWS and CircleCI
- 📝 Your AI Football Assist Eval Guide
- 📝 Find Your Code! Scaling a LlamaIndex and Qdrant Application with Google Kubernetes Engine
- 📝 Building a Serverless Application with AWS Lambda and Qdrant for Semantic Search
- 📝 Multimodal LLM with Qdrant and Gemini
- 📝 RAG App with AWS CDK, Qdrant and LlamaIndex
- 📝 Building a Multimodal LLM Application with PyMuPDF4LLM
Medium • Zilliz • Artifex • DEV • DataCamp • CircleCI • Decoding ML • AWS
- Stack: OpenAI, Hugging Face, MongoDB, Comet ML, Opik, ZenML
- Infrastructure: Comet ML
- Features: LLM evaluation, observability, workflow automation
- Stack: Docker, Qdrant, AWS EKS Fargate, AWS RDS, FastAPI, Guardrails AI
- Infrastructure: AWS
- Features: Hybrid search, auto-scaling, load balancer
- Stack: LangGraph, AWS Lambda, AWS Bedrock, Docker, CircleCI
- Infrastructure: AWS
- Features: Agent coordination, workflow automation, scalable deployment
- Stack: AWS Bedrock, Claude 3, AWS Lambda, AWS DynamoDB, AWS CDK
- Infrastructure: AWS
- Features: Text + image processing, event-driven architecture, serverless
- Stack: Quix Streams, MLflow, Kubernetes, Kafka, Grafana, PostgreSQL
- Infrastructure: Kind/Civo
- Features: Real-time inference, model registry, data validation, automated monitoring
- Stack: Databricks, MLflow, LightGBM
- Infrastructure: AWS/Databricks
- Features: Automated feature engineering, model monitoring, A/B testing
- Stack: AWS SageMaker, TensorFlow, Comet ML, Flask
- Infrastructure: AWS
- Features: Automated retraining, cost optimization
End-to-end data engineering with modern tools
- Stack: Mage, dbt, BigQuery, Terraform, Looker
- Infrastructure: GCP
- Features: Data quality checks, automated testing, visualization
| Category | Description |
|---|---|
| 🤖 ML/MLOps | Production-grade systems with comprehensive DevOps |
| 🧠 AI & LLM | Advanced AI applications and multi-modal systems |
| 📊 Data Analysis | End-to-end data science with advanced modeling |
| 🔧 Data Engineering | Scalable pipeline solutions with modern tools |
| 🛠️ Miscellaneous | Utility applications and specialized tools |
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