I build AI that
ships to production.

North Carolina, USA

• ▸ Building a RAG-powered generative AI system that unifies Handshake, career portals, and institutional databases, enabling natural language querying over student employment and placement data using Azure OpenAI and vector embeddings, serving 500+ students and 40+ advisors.
• ▸ Designing ETL workflows to ingest, normalize, and index Handshake API data into a unified knowledge layer powering real-time LLM-driven analytics dashboards for faculty.
• ▸ Architecting prompt evaluation frameworks to reduce hallucination risk and ensure reliable structured data retrieval across diverse query types.

• ▸ Designed and executed A/B tests across 3 marketing campaign cohorts using chi-square hypothesis testing, identifying high-value customer segments and improving conversion decisions.
• ▸ Built K-means customer segmentation models in Python (scipy, statsmodels) identifying 4 distinct clusters, reducing targeting spend waste and improving messaging relevance.
• ▸ Automated ETL workflows and built real-time A/B monitoring dashboards, cutting analyst reporting time by ~40%.

• ▸ Engineered 5+ production AR Lenses in Lens Studio using computer vision and 3D rendering optimization, deployed across distributed cloud infrastructure serving millions of concurrent users at sub-50ms latency.
• ▸ Optimized rendering pipelines across 3 development cycles through systems-level performance profiling, improving frame rates and cutting memory footprint by 15%.
• ▸ Built A/B testing infrastructure with event tracking and distributed analytics pipelines, identifying UX bottlenecks and driving a 20% lift in key engagement metrics.

• ▸ Built end-to-end distributed logistics optimization system using Python, scikit-learn, and TensorFlow, delivering $3.2M in annual cost savings with 23% delivery efficiency improvement across six enterprise clusters.
• ▸ Engineered a domain-specific Knowledge Graph in Neo4j modeling 500+ entities across 12 semantic dimensions, integrating Azure OpenAI with advanced prompt engineering to achieve 97% ontology accuracy on a production-grade LLM knowledge base.
• ▸ Developed automated document intelligence pipeline using fine-tuned transformer models and Hugging Face, achieving 96% extraction accuracy and reducing manual processing overhead by 80%.

• ▸ Architected and deployed a multilingual generative AI legal chatbot using InLegalBERT and ElevenLabs TTS, delivering accurate NLP responses across 10 languages to 2,000+ users.
• ▸ Designed systematic prompt engineering and LLM evaluation pipelines, improving translation reliability by 35% for low-resource Indian regional languages.
• ▸ Built scalable modular backend using FastAPI and Firebase with secure REST APIs, role-based authentication, and behavioral analytics supporting 3,000+ active users at production-grade uptime.

• ▸ Designed 500+ adversarial and edge case prompts to systematically expose failure modes in large language models including hallucination, instruction drift, and reasoning collapse across complex multi-turn conversations.
• ▸ Conducted red teaming evaluations across 3,000+ model responses, stress-testing alignment boundaries and surfacing vulnerabilities in LLM reasoning consistency.
• ▸ Maintained 90%+ agreement rate against ground truth labels, contributing high-quality feedback to RLHF pipelines used in production model training.

• ▸ Designed and deployed a production data engineering pipeline integrating PostgreSQL and MongoDB via REST APIs, improving data processing efficiency by 30% for research stakeholders.
• ▸ Automated metadata governance workflows using the OpenMetadata API, improving data discoverability and lineage tracking across the organization's data ecosystem.
• ▸ Built an interactive Streamlit data application delivering a self-serve interface for stakeholders to manage and explore organizational data assets independently.

• ▸ Architected and deployed a generative AI mental health support chatbot using GPT-3 and transformer-based NLP, boosting user engagement by 40%.
• ▸ Fine-tuned NLP algorithms combining BERT embeddings and GPT-3 language generation, improving personalized advice accuracy by 30% for end users.
• ▸ Implemented a continuous feedback loop updating chatbot response quality from live user interaction data, improving conversational accuracy by 25%.

• ▸ Led optimization of a large-scale Python web scraping framework using multithreading and async I/O, reducing data extraction time by 30% across high-throughput production scraping jobs.
• ▸ Enhanced geospatial preprocessing pipelines using DBSCAN clustering and Folium, improving spatial accuracy by 20% for real estate property evaluation at scale.
• ▸ Designed a cross-validation algorithm reducing data discrepancies by 25% across multiple large-scale property data sources.