AI/ML Engineer with 3+ years productionizing Generative AI and large-scale ML systems, currently at Meta, previously a Data Scientist at Qualcomm. I build RAG pipelines, agentic workflows, and real-time data systems, and I care most about the unglamorous parts that make them trustworthy: retrieval quality gates, drift detection, deployment safety, and reproducible benchmarks.
- 🔭 Currently: multi-agent RAG platforms with LangGraph, hybrid search over 20M+ documents, LLMOps pipelines on Kubernetes
- 🧪 Every repo below ships with committed benchmarks, measured test coverage, ADRs, and failure-mode docs; every number is reproducible from a seed
- 🎓 M.S. Information Systems (Applied AI), Pace University
- ⚡ The theme across my work: make ML systems provably reliable, not plausibly reliable
| enterprise-multi-agent-knowledge-platform | Hybrid RAG (FAISS + BM25 + RRF) behind a LangGraph supervisor: p50 latency down 91.9% under load |
| model-canary-gate | Shadow-gated canary rollouts for ML services: 0 false rollbacks in 10 identical-model trials |
| stream-anomaly-sentinel | Exactly-once streaming anomaly detection for fab telemetry: 15.7k events/s, sha256-proven replay |
| ab-decision-engine | Always-valid sequential A/B testing: peeking-safe by construction, 27.5% → 1.7% false winners |
| agent-flight-recorder | Tamper-evident record/replay for LLM agents: 50/50 byte-identical replays, budget-enforced |
| prompt-contract-ci | Contract testing for LLM structured outputs: 20,000/20,000 verdicts vs ground truth |
"In God we trust. All others must bring data." - W. Edwards Deming