Lead Applied AI/ML Engineer (Solutions Architecture) @ Databricks | Author | Open Source Contributor
Building ML platforms at scale. Helping enterprises ship AI from prototype to production.
Packt Publishing, 2023 | 244 pages
End-to-end guide for building production ML systems on Databricks - from data engineering to MLOps. Reached best seller status in its category within 2 weeks of release.
A research program examining how AI systems fail when deployment conditions differ from training/calibration:
| Paper | Focus | arXiv |
|---|---|---|
| The Semantic Illusion | Embedding-based hallucination detection fails on RLHF outputs (95% coverage → 100% FPR) | 2512.15068 |
| ATCB | Agents don't know when they'll fail—inverse accuracy-calibration relationship | Code |
| ConformalDrift | Conformal guarantees collapse under shift (95% → 11% coverage) | Code |
| DRIFTBENCH | RAG reliability degrades over time while SFR persists at 12% | Code |
| Paper | Venue |
|---|---|
| Demystifying Large Language Models | IJCET |
| Reinforcement Learning for Real-World Impact | IJSRCET |
| AI in Education: Opportunities and Challenges | IAEME |
| AI in Healthcare: Data to Patient Outcomes | IRJMETS |
Active contributor to MLflow (23K+ stars) - the leading open-source ML lifecycle platform.
Recent PRs:
- #19152 -
inference_paramssupport for LLM Judges (Approved) - #19237 - Phoenix & TruLens third-party scorer integrations
- #19238 - Async predict support for ChatModel/ChatAgent
- #19248 - Configurable parallelism for GenAI evaluation
- TechFutures 2025 (NYC) - End-to-End MLOps Pipelines Workshop (GitHub)
- Data Con LA 2022 - Simplifying AI/ML using Databricks Feature Store (YouTube)
- Data Con LA 2021 - Detecting Fake Reviews at Scale using Spark and John Snow Labs (YouTube)
- NYU Guest Lecture - ML Pipeline with Apache Spark
