AI/ML engineer building systems that work in the real world. LLM pipelines, multi-agent architectures, NLP at scale. Research to production.
I'm a Data Science graduate from NYU (May 2026) with 3+ years across ML engineering, NLP research, and data engineering. My work spans LLMs, agentic AI, healthcare applications, and production ML pipelines. I care about the full journey from messy data to something deployed and reliable.
Previously a Machine Learning Engineer at CGI for 2 years, building NER pipelines on 2M+ documents for a Fortune 500 telecom client. Also at The Global Consortium of Nursing & Midwifery Studies (multilingual NLP across 40+ languages) and Accenture (data engineering). Most recently research at NYU Rory Meyers applying BERT and statistical modeling to global healthcare data.
Outside work, I'm a Women in Data Science Ambassador (2026) and enjoy the intersection of AI with healthcare, building systems that reduce friction in clinical workflows and improve patient outcomes. When I'm not coding, I love painting, reading, and gardening — you can find my art on Instagram ↗.
Multi-agent AI platform helping NYC tenants document housing violations and auto-generate formal complaints. 3-agent A2A pipeline (Interacting → Inspection → Filing) with Gemini Live API for real-time voice intake and multimodal image analysis per NYC Housing Maintenance Code. Built at NYC Open Data Week, judged by engineers from Meta, Bloomberg, Instagram & Google.
Profiled and optimized a distributed multi-agent AI system. Cut latency 17% and achieved 12,000× throughput on cached queries using async Python, TCP connection pooling, and response caching.
Production-grade HTML quality detection pipeline achieving 0.98 precision / 0.92 F1. Combined BeautifulSoup + XGBoost with rule-based heuristics. Built a GPT-4.1-mini few-shot labeling pipeline to expand an imbalanced dataset and fully eliminated manual QA bottlenecks.
Fine-tuned Qwen2.5-Coder-7B with LoRA + PPO-Lagrangian reinforcement learning to decide when to ask clarifying questions under a strict budget. Achieved +6.2pp improvement in code correctness over baseline on HumanEvalComm benchmark.
Benchmarked emotional intelligence of Gemma, Qwen, and Llama using zero-shot and few-shot prompt engineering. All models exceeded chance performance. Evaluation orchestration via LangChain + HuggingFace.
Mario-style browser game turning 36-session cardiac rehab into an interactive world-map adventure. Gemini API as in-game AI guide. High-contrast pixel-art UI with Framer Motion. Deployed on Vercel.
End-to-end voice disease prediction deployed on Raspberry Pi. STT → NLP symptom extraction → Random Forest → TTS feedback. 95.02% accuracy. Received ₹37,000 in govt. funding. Published in IJERT.
Digital health system for chronic vertigo combining prompt engineering with Figma UX. Visualizes balance stability via Internal Compass with adaptive audio and textual guidance.
Autonomously detects niche microtrends and launches a full e-commerce store in minutes. CEO Orchestrator Agent coordinates Nimble (trend scraping), ClickHouse (data storage), and Datadog-monitored sub-agents to go from trend signal to live storefront end-to-end.
I'm graduating from NYU in May 2026 and actively looking for full-time roles in ML engineering, applied AI, NLP research, and data science.
Open to research collaborations, interesting problems, and conversations about building AI that actually ships. Response time: under 24 hours.