Machine Learning Engineer
& Data Scientist
I build AI systems that work in the real world: LLM pipelines, multi-agent architectures, and NLP at scale. NYU MS Data Science, May 2026.
Building AI that works
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 unstructured data to something reliable in production.
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, conducting 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. When I'm not coding, I love painting, reading, and gardening. You can find my art on Instagram ↗.
Work Experience
- Statistical modeling on survey data from 90+ countries to perform pattern recognition in healthcare during COVID-19.
- Built an interactive Tableau dashboard to visualize findings for the organization's public-facing website.
- BERT-based multi-class classifier achieving 87% accuracy on 5,000+ survey responses on violence faced by medical professionals.
- Analyzed 21,000+ textual survey responses using multilingual RoBERTa (HuggingFace) across 40+ languages on PPE usage and funding during COVID-19.
- Applied sentiment analysis and topic modeling to surface cross-country trends for public health reporting.
- Led the NER implementation as SME for a Fortune 500 telecom client, extracting text and tabular entities from 2M+ unstructured documents using SpaCy.
- Designed a structured database from extracted entities, reducing manual review time by 71%.
- Built an automated classification system (Flair + FastText) to categorize 10,000+ employee and client reviews for C-suite FY2023 strategic planning.
- Migrated relational database tables to Hadoop File System (HDFS).
- Generated 500+ config files, HQL query files, and AutoSys Job Boxes using the Spark engine for data ingestion pipelines.
Projects
Multi-agent AI platform helping NYC tenants document housing violations and auto-generate formal complaints. 3-agent A2A pipeline with Gemini Live API for real-time voice intake and multimodal image analysis. Judged by engineers from Meta, Bloomberg, Instagram & Google.
Profiled and optimized a distributed multi-agent AI system. Cut latency by 17% and achieved 12,000x 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 and GPT-4.1-mini few-shot labeling to eliminate 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.
Built for the AI/ML stack
Degrees & Honors
Say hello, let's build something
Open to full-time roles in ML engineering, applied AI, and NLP. Based in New York and open to relocation.
