Supreet Kaur

Working in the Frontier AI team at AWS for Startups, helping founders turn bold AI ideas into real-world products.

1. Advised and partnered with C-suite leaders at top U.S. banks to define enterprise AI strategy; delivered 20+ GenAI education sessions (Agents, RAG, Fine-tuning, etc.) to 1,000+ professionals, accelerating adoption of AI across business functions. 2. Led architecture deep-dives for production-grade AI workloads, enabling scalable solutions in chatbots, credit modeling, fraud detection, and agent-based automation across 20+ financial institutions. 3. Developed 10+ GenAI proofs-of-concept (RAG and Agents), contributing to a 40% year-over-year increase in Azure consumption within client organizations.

1. Spearheaded the development of a groundbreaking strategy to measure the efficacy of AI products, leading to a 2X increase in ROI and enhanced data-driven decision-making. (Patent Granted) 2. Led the development of a personalization engine in Azure and AWS using deep learning models, achieving a 6X increase in net new assets and demonstrating robust and agile project management skills. (Patent filed) 3. Built an ML-powered product tailored for small business owners prospects by integrating external and internal datasets through fuzzy matching and Python-based APIs. This targeted approach increased small-business engagement, catalyzing a 2X expansion in business growth.

1. Designed a BERT-based classification system on GPU platforms, enhancing patient experience categorization and driving client revenue growth by 20% 2. Developed a prospecting model utilizing TensorFlow k-means clustering to efficiently target and prioritize potential leads, improving the conversion process by 2X. 3. Led a hybrid cloud migration strategy employing Kubernetes, focusing on optimizing for GPU cloud instances. This strategy ensured the seamless integration of on-premise data centers with cloud technology. 4. Analyze and visualize data for various clients to drive business and marketing decision-making; created a dashboard to capture patient journeys and highlight product performance against competitor product lines.

1. Developed and optimized RNN and LSTM time-series forecasting models to enhance client demand planning, reducing write-offs by 35%. 2. Developed a proof of concept for detecting similarity in the launch trajectories of different products in different markets by leveraging various clustering techniques. 3. Outlined AI project scopes and established roadmaps for effective resource management allocation, supporting clients in formulating benchmark strategies, and assessing AI project ROI.

• Researched, designed, and implemented advanced machine learning systems using predictive modeling and natural language processing. • Managed Big Data for training and deploying ML/NLP solutions, enhancing business modules and search bots. • Conducted data cleansing, feature engineering, and statistical analysis to support R&D initiatives.

I was selected for the prestigious summer leadership program, where I led a project to predict the probability of claims for vehicle policies. By analyzing data in R, SAS, and SQL, I developed a risk scorecard that reduced operational risk by 20%. My findings were presented to the executive team, including the CEO and CFO, showcasing the impact of data-driven insights on strategic decisions.

• Accomplished CITI training to get access to MIMIC-III data. • Data Wrangling for the purpose of prediction and analysis. • Logistic Regression was employed to predict the mortality of ICU patients using various vital signals. • Data Visualization was performed using Tableau.