Ajay Borra

About Ajay Borra

Hello there, my name is Ajay!I’m an Associate Applied AI/ML Scientist at Sprout Social, where I develop different machine learning and AI solutions that transform complex data and tasks into clear, actionable insights and real-world use cases. I am passionate about creating practical, human-centered AI solutions and feel grateful to contribute to impactful work every day.Previously, I’ve worked across healthcare innovation, edtech, and research, applying deep learning, LLM pipelines, and advanced data analysis to solve challenging problems. From using GPT-4 Vision to build multimodal food recognition tools to fine-tuning TiDE models for sensor glucose forecasting at Medtronic, I’ve enjoyed applying ML in ways that merge technical depth with meaningful outcomes.My interests include natural language processing, deep learning, computer vision, and multi-modal learning, and I’m especially excited about the intersections of neuroscience and AI. With a background in neural science and computer science from NYU, I aim to bring a unique perspective to the development of applied AI/ML systems that improve people’s lives.Technically, I am proficient in Python, Java, Kotlin, Matlab, SQL, and various ML frameworks including PyTorch and TensorFlow, and I strive to remain current with evolving methods in machine learning and applied AI.Outside of work, I’m committed to responsible, accessible AI and enjoy exploring open-source models and agentic workflows in my spare time. Apart from the AI world, I love to travel and explore new places and cultures!I’m always open to connecting with others who are passionate about the future of applied AI and machine learning. Let’s connect!

Experience

1. Associate Applied Ai Ml Scientist

   Sprout Social, Inc.

   Jun 2025 — Present

   Generative AI team• Owned and scaled a standardized evaluation framework for generative AI, bringing 12 out of 16 of our AI features (75%) onto industry aligned quality metrics and reducing evaluation cycles from weeks to days, accelerating feature iteration and launch velocity• Led evaluation and testing efforts for a feature beta launch, informing feature design and building an LLM-as-Judge evaluator that reduced feature iteration and testing time from days to ˜1 hour; contributed to beta achieving 62% repeat usage and 100% positive collected ratings across ˜100 beta users within 30 days• Owned model EOL migration testing for 4 production AI features under compressed timeline, adapting evaluation strategies for social network contract compliance, enhancing customer experience and reducing feature LLM costs by at least 50%• Developed internal Streamlit tools and dashboards for non-technical stakeholders to independently conduct AI evaluations and make data-driven product decisions, speeding up collaboration among AI, Product, and Engineering teams• Mentored and onboarded peers on standardized evaluation methodology, enabling parallel workstreams and scaling AI testing practices across the team to support organizational AI monthly active user adoption goals

Education

• New York University

  Bachelor of Science - BS, Neural Science (computational focus), minor in Computer Science and Mathematics