Christopher J. Bratkovics

Data Scientist → AI Engineer

I ship production LLM systems, RAG pipelines, and predictive models with verifiable benchmarks

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.

0
Production Projects
0.1%
Best Model Accuracy
~0ms
P95 Latency
0%
Docker Reduction (RAG)
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Technical Arsenal

Demonstrated expertise in production ML systems - all skills verifiable through GitHub projects

Core AI Engineering

LLM Orchestration (OpenAI/Anthropic)RAG (ChromaDB + BM25)Semantic Caching (~73% hit rate)WebSocket StreamingFailover Patterns

MLOps

FastAPI ServingCI/CD (GitHub Actions)Drift Detection (KS/Chi-squared)MLflow/MonitoringA/B Testing

Systems

RedisPostgreSQLDocker/K8sPrometheus/Grafana/JaegerJWT + RSA Auth

ML/AI Models

XGBoostLightGBMNeural NetworksFeature EngineeringSHAP Explainability

Backend & APIs

FastAPIAsyncIOCelerySQLAlchemyWebSockets

Data & Tools

PythonSQLGitPandasNumPyJupyter

Production Focus

Specialized in building production-ready ML systems with 93.1% accuracy, ~186ms P95 latency, and 88% 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

Multi-Tenant AI Chat Platform

~73% Cache Hit Rate

Production chat service with semantic caching achieving ~70% cost reduction

~186ms
P95 Latency
~73%
Cache Hit Rate
~70-73%
Cost Reduction
100+ verified
Concurrent Users
Performance
No caching~73% cache hit
~70% cost reduction

Key Features

  • ▸P95 latency ~186ms with 100+ concurrent WebSocket sessions (verified)
  • ▸Semantic cache ~73% hit rate with ~70-73% API cost reduction (JSON artifacts)
  • ▸Provider failover ~463ms between OpenAI and Anthropic
OpenAIAnthropicFastAPIWebSocketsRedisPostgreSQLJaeger
Source CodeView Benchmarks

Enterprise Document Intelligence (RAG)

P95 <200ms

Hybrid retrieval system with verified metrics

42%
Cache Hit Rate
<200ms
Query Latency P95
88%
Docker Reduction
+35%
Relevance Boost
Performance
3.3GB Docker image402MB Docker image
88% reduction

Key Features

  • ▸Hybrid retrieval (ChromaDB + BM25) with P95 <200ms
  • ▸42% semantic cache hit rate (verified)
  • ▸Docker 3.3GB → 402MB (−88% reduction)
LangChainChromaDBFastAPICeleryRedisDockerOpenAI
Source CodeView Benchmarks

Fantasy Football AI Platform

93.1% Accuracy

Weighted ensemble achieving 93.1% accuracy

93.1%
Model Accuracy
<100ms cached
API Latency
100+
Features Engineered
XGB, LGBM, NN
Ensemble Models

Key Features

  • ▸Weighted ensemble (XGBoost, LightGBM, Neural Networks) reaching 93.1% accuracy
  • ▸Feature store with 100+ engineered features (verifiable in code)
  • ▸Redis caching achieving <100ms cached, <200ms uncached
XGBoostLightGBMNeural NetworksFastAPIRedisPostgreSQLCelery
Architecture: Repository pattern with clean architecture
Source CodeView Benchmarks

NBA Performance Prediction System

R²: 0.942

ETL pipeline processing 169K+ records with drift detection

0.942
Points R²
87ms
API P95
169K+
ETL Records
40+
Features
Performance
Manual analysis87ms P95
Automated pipeline

Key Features

  • ▸R² 0.942/0.887/0.863 (pts/reb/ast) on 169K+ records
  • ▸P95 latency 87ms with Redis caching (verified)
  • ▸Drift detection using KS and Chi-squared tests
XGBoostFastAPIPostgreSQLRedisMLflowSHAP
Source CodeView Benchmarks

SQL Intelligence Platform (Design Phase)

Design Targets

Multi-tenant architecture with natural language SQL

<500ms P95
Target Gen
Per-tenant DB
Isolation
JWT+RSA
Auth
5000+
Target RPS

Key Features

  • ▸Design target: Row-level security with database-per-tenant isolation
  • ▸JWT authentication with RSA key rotation (implemented)
  • ▸Target: P95 <500ms SQL generation
FastAPIPostgreSQLJWTRedisDockerKubernetes
Source CodeView Benchmarks

Benchmark Methodology

Local synthetic benchmarks on developer hardware. We publish P50/P95/P99, cache hit rate, and cost deltas. See linked JSON artifacts for reproducibility.

Chat Platform

k6 WebSocket tests, 100+ concurrent (local synthetic)

P50/P95/P99 latency (~186ms P95), cache hit (~73%), cost reduction (~70%)

RAG System

Custom eval sets, production metrics

P95 <200ms, 42% cache hit, Docker −88%

Fantasy AI

Historical data, k-fold cross-validation

93.1% accuracy, 100+ features, <100ms cached

NBA Predictions

169K+ game records, time-aware validation

R² 0.942 (points), P95 87ms

Re-run steps and artifacts linked on every project page

Real-World Production Impact

Verifiable achievements from production ML systems and automation

0+
Weekly Hours Saved
Through Python ETL automation (verified)
0.1%
Best Model Accuracy
Fantasy Football ensemble (verified)
0
Players/Second
Feature engineering pipeline
0K+
Records Processed
NBA ETL pipeline (verified)
4
Production Projects
With verified benchmarks
~186ms
P95 Latency
Chat platform (synthetic)
88%
Docker Reduction (RAG)
3.3GB → 402MB

Demonstrated Engineering Practices

Clean ArchitectureRepository PatternCI/CD with GitHub ActionsPerformance MonitoringRedis CachingMulti-tenant DesignJWT AuthenticationDocker Optimization

Let's Build Together

Ready to transform your ML models into production-ready systems? Let's discuss how I can help.

Quick Connect

View source code for all projects on GitHub - all metrics verifiable

GitHub
LinkedIn

© 2025 Christopher Bratkovics. Built with Next.js, TypeScript, and Tailwind CSS.

All metrics from GitHub repositories | Synthetic benchmarks noted with (~)