Prashant

I’m a software engineer and AI-focused data professional with over five years of experience working at the intersection of backend development, data analysis, and large language models. My core strength lies in turning complex ideas into practical, reliable systems—whether that’s building scalable APIs with Python and FastAPI, designing full-stack applications with React and Node.js, or developing AI-powered tools using modern LLM workflows like RAG and multi-model pipelines.

Over the years, I’ve worked extensively in roles that demand both technical depth and precision, including prompt engineering, LLM evaluation, data quality assurance, and ML-driven product development. At organizations like Scale AI and Alignerr AI, I’ve contributed to improving model performance by validating outputs, designing better prompts, building evaluation frameworks, and ensuring training data meets high quality standards. This experience has given me a strong understanding of how real-world AI systems behave—and how to make them more accurate, reliable, and useful.

My background in data science and machine learning research, including work on satellite and aerial image segmentation, has shaped the way I approach problems: with a balance of theory, experimentation, and practical execution. I enjoy working with data end-to-end—cleaning it, analyzing it, building systems around it, and finally turning it into insights or products that people can actually use. Lately, my focus has been on building AI-powered data analysis tools where users can upload datasets or documents and get meaningful summaries, insights, and visualizations through LLMs.

What sets me apart is my blend of engineering, AI, and quality-focused thinking. I don’t just aim to make things work—I aim to make them robust, scalable, and trustworthy. I’m comfortable collaborating with researchers, engineers, and non-technical stakeholders, and I care deeply about clarity, documentation, and long-term maintainability.

At my core, I’m someone who enjoys solving messy, real-world problems with clean, well-thought-out solutions. I’m constantly learning, experimenting with new technologies, and pushing myself to build systems that are not just technically sound, but genuinely impactful.