Christopher J. Bratkovics
Data Scientist → AI Engineer | Building Production ML Systems
About Me
I’m a Data Scientist evolving into AI Engineering, focused on transforming ML research into reliable production systems. My journey spans from statistical analysis and predictive modeling to building scalable ML platforms with modern engineering practices. Each project below demonstrates progression toward production-ready AI systems.
Technical Portfolio
Production ML Systems
Document Intelligence RAG System
Enterprise RAG implementation with hybrid search and async processing
Architecture Highlights:
- Document processing pipeline supporting PDF, TXT, Markdown formats
- Hybrid search combining vector similarity (ChromaDB) with BM25 keyword matching
- Asynchronous document processing with Celery task queue
- Streaming response support for real-time interactions
- Prometheus metrics integration for observability
Engineering Decisions:
- Implemented chunking strategies preserving semantic boundaries
- Used factory pattern for multiple embedding model support
- Applied circuit breaker pattern for external API resilience
- Structured with clean architecture (ports and adapters)
Key Components:
- Smart document chunking with overlap for context preservation
- Cross-encoder reranking for improved relevance
- Health check endpoints with component-level status reporting
- Comprehensive error handling and retry logic
Technical Stack: LangChain, ChromaDB, FastAPI, Celery, Redis, OpenAI APIs
GitHub
Enterprise platform with natural language SQL generation
Architecture Highlights:
- Multi-tenant architecture with database-per-tenant isolation
- JWT authentication with RSA key rotation
- Cost tracking and usage monitoring per tenant
- Automated backup and disaster recovery system
- Kubernetes-ready with Helm charts
Engineering Decisions:
- Implemented tenant context injection via middleware
- Used event sourcing for audit logging
- Applied CQRS pattern for read/write separation
- Structured with domain-driven design principles
Security & Compliance:
- Input validation against SQL injection
- Row-level security in PostgreSQL
- Encrypted secrets management
- Comprehensive audit logging
Technical Stack: FastAPI, PostgreSQL, JWT, Redis, Docker, Kubernetes
GitHub
Production chat system with intelligent routing and caching
Architecture Highlights:
- Multi-model integration (OpenAI, Anthropic) with fallback logic
- WebSocket implementation for real-time streaming
- Semantic caching reducing API costs by ~30%
- Distributed tracing with Jaeger
- Auto-scaling based on request patterns
Engineering Decisions:
- Implemented adapter pattern for LLM provider abstraction
- Used strategy pattern for model selection logic
- Applied saga pattern for distributed transactions
- Built with hexagonal architecture principles
Performance Optimizations:
- Connection pooling for database operations
- Request batching for embedding generation
- Lazy loading of ML models
- Response streaming to reduce perceived latency
Technical Stack: FastAPI, WebSockets, Redis, PostgreSQL, OpenAI/Anthropic APIs
GitHub
Data Science & Analytics Projects
Ensemble ML system with production API and intelligent caching
Architecture Highlights:
- Ensemble model combining XGBoost (0.4 weight), LightGBM (0.35), and Random Forest (0.25)
- FastAPI backend with async request handling and Pydantic validation
- Redis caching layer with TTL strategies for frequently accessed predictions
- Docker multi-stage builds reducing image size from 3.3GB to ~1.5GB
- PostgreSQL with SQLAlchemy ORM for data persistence
Engineering Decisions:
- Implemented feature store pattern with 50+ engineered features
- Used repository pattern for data access layer abstraction
- Applied dependency injection for testable components
- Structured as microservices-ready monolith for future scaling
Technical Stack: Python, FastAPI, Redis, PostgreSQL, Docker, XGBoost, LightGBM
End-to-end ML pipeline with comprehensive feature engineering
Data Engineering:
- ETL pipeline processing 169K+ game records
- Automated data validation and cleaning
- Feature engineering pipeline with 40+ computed features
- Time-series analysis for player momentum tracking
Model Development:
- Gradient Boosting and Random Forest ensemble
- Achieved R² scores: Points (0.942), Rebounds (0.887), Assists (0.863)
- Comprehensive hyperparameter tuning with Optuna
- Cross-validation with temporal splits
Production Features:
- Batch prediction API endpoint
- Model versioning and rollback capability
- Performance monitoring dashboard
- A/B testing framework for model comparison
Technical Stack: Python, Scikit-learn, XGBoost, FastAPI, PostgreSQL, Pandas
GitHub
Technical Skills Evolution
Current Focus (AI Engineering)
- API Development: FastAPI, async/await patterns, REST design
- System Architecture: Microservices, event-driven, clean architecture
- MLOps: Model versioning, A/B testing, monitoring, CI/CD
- Infrastructure: Docker, Kubernetes, Redis, PostgreSQL
- Production ML: Feature stores, model serving, caching strategies
Foundation (Data Science)
- ML/DL: Scikit-learn, XGBoost, TensorFlow, PyTorch
- Data Engineering: Pandas, NumPy, SQL, ETL pipelines
- Statistical Analysis: Hypothesis testing, time series, experimentation
- Visualization: Matplotlib, Plotly, Streamlit
Architecture Patterns & Best Practices
Throughout these projects, I’ve implemented:
Design Patterns:
- Repository pattern for data access abstraction
- Factory pattern for model/provider selection
- Strategy pattern for algorithm switching
- Adapter pattern for external service integration
Architecture Principles:
- Clean architecture with dependency inversion
- Domain-driven design for complex business logic
- Event-driven architecture for loose coupling
- Microservices-ready monolith design
Engineering Practices:
- Comprehensive error handling and logging
- Health checks and readiness probes
- Graceful degradation and circuit breakers
- Feature flags for progressive rollouts
Professional Impact
At OUTFRONT Media (Current):
- Deployed ML models to production with monitoring and A/B testing
- Built MLOps workflows for automated model training and deployment
- Integrated Generative AI solutions into production systems
Key Achievements:
- Reduced model deployment time from weeks to hours
- Implemented automated retraining pipelines
- Built real-time prediction APIs serving 1000+ requests/day
What I Bring
Production Mindset: Every project is built with deployment in mind - from error handling to monitoring to scaling considerations.
Full-Stack ML: I bridge the gap between model development and production systems, understanding both the math and the engineering.
Best Practices: Clean code, comprehensive testing, documentation, and architectural patterns that scale.
Currently Learning
- Advanced Kubernetes orchestration for ML workloads
- Feature stores (Feast) for production ML
- Real-time ML with Apache Kafka and Flink
- Model optimization techniques (quantization, distillation)
Let’s Connect
I’m passionate about making ML work in production. If you’re looking for someone who can both build models and deploy them reliably at scale, let’s talk.
Contact: LinkedIn
Note: All code is available in the linked repositories with detailed documentation, setup instructions, and architectural decision records.