Author: Samruddhi Bhabad

Samruddhi Bhabad

Short Bio

Samruddhi Bhabad is a dedicated Computer Science learner who thrives on innovation and leadership. She has led teams to victory in 6 national hackathons and is a 10x hackathon winner overall. She is skilled in AI-powered chatbots, scalable web architecture, and REST and FAST APIs, with proficiency in TensorFlow, Python, and Django. As an ML & AI Engineer currently pursuing Computer Science at UMass Amherst, she is passionate about developing AI-driven solutions that push the boundaries of technology. Through internships and research at Intel, Amazon, and global startups, she has engineered LLMs, RAG pipelines, and NLP systems that improved model accuracy by up to 40% and reduced response times by 30%. She bridges research and production, optimizing models for real-world impact. Samruddhi is a versatile software enthusiast with a passion for innovative solutions and a track record of impactful projects in web development, machine learning, and data science.

Extended Bio

Samruddhi Bhabad builds AI that works when it matters. She is an ML & AI Engineer and a Computer Science student at UMass Amherst, with a track record that most professionals would envy — and she is just getting started.Her approach to AI is grounded in a simple belief: models that perform well in research papers are only valuable if they perform well in the real world. Through internships and research roles at Intel, Amazon, and global startups, she has engineered Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and NLP systems that deliver measurable results — improving model accuracy by up to 40% and reducing response times by 30%. She doesn't just build models; she optimizes them for deployment, for scale, and for the messy, unpredictable conditions of real-world use.Her technical toolkit is broad and deep: TensorFlow, Python, Django, REST and FAST APIs, and scalable web architecture. She has developed AI-powered chatbots and built systems that handle production traffic. She is equally comfortable working on the infrastructure that makes AI run at scale as she is fine-tuning the models themselves.Samruddhi is also a proven leader and competitor. She has led teams to victory in 6 national hackathons and is a 10x hackathon winner overall — records that speak to her ability to perform under pressure, collaborate effectively, and deliver working solutions on tight timelines. She is a versatile software enthusiast with a passion for innovative solutions and a track record of impactful projects spanning web development, machine learning, and data science.As an author for ENTECH Magazine, Samruddhi writes for the aspiring ML engineer navigating their career, the hackathon competitor looking for strategies to win, the computer science student building their portfolio, the engineer transitioning from research to production, and anyone who believes that the best AI is the AI that actually works.

Primary Beats

• LLMs, RAG Pipelines & NLP Systems — From Research to Production: Engineering large language models and retrieval-augmented generation systems; building NLP pipelines that work at scale; the gap between research performance and production performance — and how to close it; optimizing models for accuracy, speed, and real-world reliability.
• Hackathon Strategies & Winning as a Team: Leading teams to victory in national hackathons; the mindset, preparation, and execution required to win; building and deploying working solutions under tight deadlines; how hackathon experience translates to real-world engineering capability.
• Full-Stack AI Development — Web, APIs & Scalable Architecture: Building AI-powered chatbots and web applications with Python, Django, TensorFlow; designing REST and FAST APIs that serve ML models; scalable web architecture for AI-driven products; integrating frontend, backend, and ML into cohesive systems.
• The Computer Science Journey — From Learner to Industry-Ready Engineer: Navigating a CS degree while building industry experience; internships at Intel, Amazon, and startups — what they teach you that the classroom doesn't; building a portfolio that demonstrates real impact; advice for students aiming for careers in AI and ML.