It all started when I took a Machine Learning Course in my undergrad and began working
on my project. That opened my eyes to the power of data and algorithms and how a few lines of code could
reveal hidden patterns, make predictions, and provide actionable insights. What started as curiosity
soon grew into a passion for understanding how machine learning could transform industries and improve
decision-making.
After completing my Bachelor of Engineering in Computer Science (VTU), I entered
industry as a Machine Learning Engineer at Ideapoke Technologies, Bengaluru. This role
was a turning point: I worked on large-scale text mining pipelines, NER models, and retrieval-augmented
generation (RAG) workflows using LangChain and GPT-3.5. A key contribution was improving the
Signalz product and also helped design a contextual search engine spanning 30 industries, a
true test of building scalable and high-performance systems. This experience gave me a strong grounding
in practical machine learning, collaboration with cross-functional teams, and the realities of deploying
AI in production.
That foundation led me to pursue my Master's in Data Science at George Washington
University. Here, I deepened my expertise in deep learning, NLP, reinforcement learning, time
series, and big data analysis, and became a Graduate Research Assistant on the LAiSER
Project. I was also honored to be a Recipient of the Global
Leaders Scholarship Award for 2024–2025. I built an AI-driven framework for skill extraction,
taxonomy alignment, and knowledge graph integration. My work included deploying APIs on AWS, developing
clustering pipelines, and even designing the UI/UX for the LAiSER platform. Presenting LAiSER at the 2025 Badge Summit Conference was a
milestone, where I presented a comprehensive overview of the LAiSER platform,
highlighting its unique features and the challenges it addressed that resonated with educators,
workforce leaders, and researchers.
Alongside research, hackathons became a key part of my growth. From building a real-time coding game at
HoyaHacks, to analyzing World Bank jobs data at the DataDive
Hackathon, and to co-developing GearGuide at the DevNetwork [AI +
ML] Hackathon 2025, each project tested my creativity and ability to turn ideas into working
prototypes. GearGuide, in particular, was a highlight, a graph-powered AI
troubleshooting chatbot, that combined OpenAI embeddings with a Neo4j knowledge base. By
designing a hybrid dense + sparse retrieval system and a custom PDF-to-Graph ingestion pipeline, we
created a multi-turn, context-aware assistant that felt less like a script and more like a real
mechanic’s companion. This project reinforced my passion for RAG systems, knowledge graphs, and building
domain-specific AI assistants.
Each hackathon reinforced my ability to go from idea → prototype → demo within tight timelines, a
skill that mirrors the pace of innovation in real-world AI development.
Looking back, my journey feels like a continuous progression:
- From a student discovering ML concepts in undergrad,
- To an engineer applying them at scale in industry,
- To a researcher and builder pushing AI applications further in graduate school.
Today, I see myself as a Data Scientist and Machine Learning Engineer who thrives at
the intersection of research, product development, and real-world impact. My goal is to continue
designing systems that not only push technical boundaries, but also create meaningful solutions for
people and communities.
Support Genie 
Badge Summit 2025
UN Open Source 2025
GearGuide 
HoyaHacks 2025
