AI Application Developer

• CGPA 3.93 / 4.
• Winner: AI in Business Case Competition; designed an AI integration strategy for small businesses.

• Built end-to-end AI/ML pipelines for large job market datasets, processing 10K+ records using Python and Snowflake for downstream machine learning tasks.
• Developed and evaluated semantic classification models, achieving 91% accuracy with full precision, recall, and F1 reporting.
• Designed robust ingestion, transformation, and feature extraction pipelines that improved processing efficiency by 40%.
• Partnered with cross-functional teams to launch production AI solutions and automate reporting workflows.

• Deployed AI-driven automation systems that cut response time from 3 hours to under a minute by integrating ML models into business workflows.
• Developed data processing pipelines and model inference services, reducing decision latency by 60%.
• Built scalable REST APIs for model serving and integrated AI solutions into client enterprise systems.

• Developed and maintained Linux automation scripts for server maintenance, monitoring, and issue resolution across production healthcare systems.
• Supported deployment, maintenance, and reliability for Cerner applications, ensuring stable high-availability performance.
• Worked on Cerner Millennium PathNet, monitoring database health and resolving workflow-impacting incidents.
• Participated in on-call rotations and resolved five high-priority incidents through troubleshooting and rapid recovery.

• Built and supported SAP ABAP solutions for WM/MM workflows, driving enhancements and defect resolution in Agile sprints.
• Resolved 350+ production tickets by debugging application issues and delivering fixes within SLA targets.
• Handled on-call support for enterprise systems, coordinating timely resolutions for high-impact issues.
• Authored a dozen knowledge base articles during the ServiceNow migration to streamline support playbooks.

• Developed an AI-powered recruitment platform that applies ML models and pipelines for candidate-job matching at scale.
• Built serverless data processing workflows via AWS Lambda and DynamoDB for real-time data ingestion and scoring.

• Designed a graph-native AI workspace enabling multiple concurrent LLM contexts to reason over persistent user knowledge graphs.
• Implemented hybrid retrieval combining Neo4j graph traversal with vector semantic search to improve factual grounding and reduce hallucination.
• Built long-term memory and session-level context persistence for multi-turn reasoning across tasks, documents, and conversations.

Smith Master's IS Association · VP Finance

Jan 2025 – Present · Developed a mentorship program connecting 100+ students and alumni; featured in a university article for AI integration.