Christopher J. Bratkovics
Data Scientist → AI Engineer
Building Production ML Systems That Ship | All Metrics Verifiable via GitHub
I bridge advanced analytics and reliable engineering to transform experimental AI into production systems that deliver real business value. From deploying ML models and RAG architectures to building low-latency inference pipelines, I thrive at the intersection of cutting-edge AI capabilities and practical engineering constraints. My mission: ensure ML solutions are not just accurate in notebooks, but scalable, monitored, and impactful once deployed. The rapid evolution in generative AI energizes me, pushing boundaries while maintaining the discipline needed for production systems.
Technical Arsenal
Demonstrated expertise in production ML systems - all skills verifiable through GitHub projects
ML/AI Engineering
Backend & APIs
MLOps & Infrastructure
AI/LLM Systems
System Design
Data & Tools
Production Focus
Specialized in building production-ready ML systems with 93% accuracy, <150ms latency, and 54% Docker optimization. Experienced in taking models from notebook to production with proper engineering practices in production environments.
Production Systems
ML systems built for scale, performance, and reliability in production environments
Production-ready ML system with ensemble models achieving 93.1% accuracy
Key Features
- ▸Ensemble model with optimal weighted voting
- ▸Feature store with 40+ engineered features (verifiable in code)
- ▸Redis caching achieving <100ms latency for cached requests
Production-ready sports analytics platform with high-accuracy predictions
Key Features
- ▸Comprehensive feature engineering (40+ features)
- ▸P95 latency of 87ms (documented in README)
- ▸ETL pipeline processing 169K+ NBA game records
Production-ready RAG system with hybrid search and semantic caching
Key Features
- ▸Hybrid search (ChromaDB + BM25)
- ▸Semantic caching reducing LLM calls by 42% (documented)
- ▸Async Celery workers for document processing
Production-ready SaaS with natural language SQL generation and tenant isolation
Key Features
- ▸Database-per-tenant isolation strategy
- ▸JWT auth with RSA key rotation
- ▸PostgreSQL with row-level security
Production-ready chatbot platform with multi-model support and WebSockets
Key Features
- ▸Multi-model support (GPT-4, Claude, Llama)
- ▸WebSocket streaming with fallback logic
- ▸Semantic caching reducing costs by 30%
Real-World Production Impact
Verifiable achievements from production ML systems and automation
Demonstrated Engineering Practices
Let's Build Together
Ready to transform your ML models into production-ready systems? Let's discuss how I can help.
© 2025 Christopher Bratkovics. Built with Next.js, TypeScript, and Tailwind CSS.
All achievements documented and open-source | Metrics verifiable via GitHub