# Sai Kamal Gayar > NYU CS Grad | ML Enthusiast | GenAI + RAG | Full-Stack (Python, Node, React) Location: New York, New York, United States Profile: https://flows.cv/saikamalgayar Results-driven AI Software Engineer / Data Scientist with 2+ years of experience building GenAI and ML-powered products across edtech, fintech, and research domains. Skilled in designing LLM-integrated systems (RAG, prompt orchestration), developing predictive and classification models (NLP, deep learning), and productionizing them through secure APIs and scalable pipelines. Strong hands-on experience with Python, SQL, and full-stack development using Node.js/TypeScript and React, alongside modern MLOps practices like Docker and CI/CD. Adept at applying statistical experimentation and analytics to improve product outcomes, and collaborating cross-functionally to deliver reliable, high-performance solutions. ## Work Experience ### AI Software Engineer @ CulturaGo Jan 2024 – Present | New York, United States • Built and maintained fullstack platforms using Spring Boot, Flask, React, and PostgreSQL, delivering performant student assessment tools, admin dashboards, and analytics portals used by 20+ university partners and internal stakeholders. • Built an LLM-powered assessment and feedback system using GPT-4 and retrieval-augmented generation (RAG) to automate rubric-aligned report generation, processing 10,000+ submissions with 95% acceptance rate. • Designed scalable, production-grade GCP data pipelines leveraging BigQuery, App Engine, and Cloud Scheduler to ingest, clean, and transform 10K+ daily video and form records from VideoAsk and HubSpot for downstream analytics and reporting. • Refactored monolithic services into modular Java/ Python microservices with event-driven architecture, enabling real-time filtering and reducing latency by 70%. • Trained and evaluated ML models (BERT, XGBoost, Random Forest, LSTM) to flag low-effort/ off-topic responses and forecast competency growth, achieving 87% accuracy in rubric-level proficiency change prediction. • Streamlined internal reporting through CI/ CD automation with GitHub Actions and Docker containerization, cutting manual workload by 85% and adding automated quality checks to monitor output drift. ### Software Engineering Intern @ EyeSpace AI Jan 2024 – Jan 2024 | New York, United States • Rebuilt a legacy EHR backend into Kubernetes-deployed Flask microservices, improving modularity, scalability, and maintainability. • Developed secure RBAC-compliant REST APIs integrated with PostgreSQL, enforcing HIPAA-aligned access policies. • Improved API performance by 40% through profiling, applying concurrency enhancements, and optimizing database indexing for datasets. • Conducted rigorous testing, deployment, and rollout of backend services used daily by 1,000+ medical staff across clinics. ### Machine Learning Analyst (Customer Analytics) @ Instamojo Jan 2021 – Jan 2022 | India • Built customer segmentation models (K-Means, hierarchical clustering) using 5M+ user events across web/app/payment flows to drive personalization, improving SME conversion by 17%. • Developed a logistic regression propensity scoring pipeline using clickstream, purchase-cycle, and geo-demographic features to identify high-intent merchants and improve campaign ROI. • Conducted A/B testing and funnel analysis across email, SMS, and ad channels using Python and statsmodels, improving conversion rates by 25% and guiding budget allocation. • Refactored and optimized 1,000+ lines of SQL (CTEs, window functions, nested queries), reducing report runtimes by 60% and improving dashboard freshness. ### Data Science Intern (Full-Stack ML) @ National University of Singapore and Hewlett Packard Enterprise Jan 2022 – Jan 2022 | Singapore • Built a full-stack crop recommendation platform with a Flask API backend and React UI, enabling real-time predictions from soil pH and nutrient inputs. • Trained and evaluated ML classifiers (SVM, XGBoost, Random Forest, ANN) using 5-fold cross-validation, ROC-AUC curves, and confusion matrices to compare precision/recall trade-offs. • Selected an ANN (TensorFlow/Keras) after comparative evaluation showed stronger generalization on non-linear relationships, achieving 92% prediction accuracy with improved minority-class recall. • Tuned the neural network (hidden layers, activations, dropout) using randomized search with early stopping and learning-rate decay, reducing overfitting and cutting training time by ~30%. ## Education ### Master's degree in Computer Science New York University Jan 2023 – Jan 2025 ### B.Tech. in Computer Science and Engineering with specializationin AI-ML SRMIST, Kattankulathur, Chennai ,Tamil Nadu ## Contact & Social - LinkedIn: https://linkedin.com/in/gayar-sai-kamal --- Source: https://flows.cv/saikamalgayar JSON Resume: https://flows.cv/saikamalgayar/resume.json Last updated: 2026-03-23