ML Engineer building production-grade AI systems with safety at the core. Currently researching Multi-Agent RL for cybersecurity at the University of Arizona and co-authoring StepShield — a safety benchmark for autonomous code agents (submitted to ICML 2026). Previously built recommendation engines at Escape LLC (30% engagement lift) and agentic RAG chatbots at Omdena (95% reduction in harmful responses).
I don't treat AI safety as a checkbox — I treat it as an engineering discipline.
|
First benchmark for evaluating when autonomous code agents go rogue — not just whether they do. Detects specification violations (data exfiltration, unauthorized access) in real-time across 9,213 agent trajectories. Early detection cuts monitoring costs by 75% (~$108M projected savings).
|
|
Production-grade ML pricing system XGBoost demand forecasting + price elasticity estimation + scipy revenue optimization. FastAPI serving, Streamlit dashboard, MLflow tracking, Evidently drift monitoring.
|
Full-stack speech pipeline: STT → LLM → TTS End-to-end voice assistant with FastAPI backend, React frontend, and Docker containerization. Speech-to-Text, LLM reasoning, and Text-to-Speech in one pipeline.
|
|
5 languages + Hindi-English code-switching Multi-task XLM-RoBERTa with LoRA adapters, ONNX INT8 inference, and cross-lingual transfer. Production-grade multilingual NLP pipeline.
|
Sentiment-aware stock prediction system Combines NLP sentiment analysis on financial headlines with quantitative indicators. TimeGPT predictions + Power BI dashboard. 20% higher prediction accuracy.
|
| Languages |
|
| ML & DL |
|
| LLM & Agents |
|
| MLOps & Cloud |
|
| Data |
|
Open to ML Engineer, AI Safety, and AI Researcher roles — remote & relocation
Let's build AI systems that are powerful AND trustworthy.