Article Digest – Proof Points

Compact proof points from portfolio projects. Read by career-ops at evaluation time.

FraudShield – Real-Time Fraud Detection

Hero metrics: 99.7% precision, 50ms p99 latency, $2M/year fraud prevented

Architecture: Kafka Streams ingestion → real-time feature computation (200+ features, sliding windows) → ensemble model (XGBoost + neural network) → decision engine with configurable thresholds → human review queue for edge cases

Key decisions:

• Chose streaming over batch to catch fraud in real-time (batch had 4-hour delay)
• Ensemble approach: XGBoost for speed + neural net for complex patterns
• Built custom feature store for real-time features (Redis-backed, 5ms reads)

Proof points:

• Reduced false positives 60% vs previous rule-based system
• Handles 10K transactions/second peak load
• 500+ GitHub stars, adopted by 3 fintech startups
• Conference talk: “Real-Time ML at Scale” (MLConf 2023)

LLM Eval Toolkit – Evaluation Framework

Hero metrics: 15 built-in metrics, CI/CD integration, used by 200+ developers

Architecture: Pluggable metric system → test suite runner → regression detection → GitHub Actions integration → Slack alerts on regressions

Key decisions:

• Metrics as code: each metric is a Python function with clear interface
• Deterministic testing: seeded prompts + temperature 0 for reproducible evals
• Cost tracking: each eval run logs token usage and estimated cost

Proof points:

• Caught 3 production regressions before deployment in first month
• Reduced eval cycle from “vibes check” to structured 15-minute CI run
• Open source, 200+ weekly active users on PyPI