Research Analyst at Edureka
Applied research and analysis skills in a fast-moving ed-tech environment resulting in educational level presentation consumed by students taking up the course for easier understanding of complex topics
I work across data science, backend APIs, and AI application development, with hands-on experience in analytics, machine learning, and modern AI technologies. My projects usually start with a messy research question and end as something someone can actually use: search tools, automated pipelines, model-backed services, or practical dashboards.
Research, production code, data pipelines, and leadership inside AI communities.
Applied research and analysis skills in a fast-moving ed-tech environment resulting in educational level presentation consumed by students taking up the course for easier understanding of complex topics
Started the Ai fever among students across the campus so that they could learn, integrate, work with Ai and understand its working by implementing it on a daily basis in theirs lives. Also lead workshops and seminars
Developed AI-powered applications and backend services using Python, FastAPI, REST APIs, and machine learning workflows, while building automated data pipelines, web scraping solutions, and data preprocessing systems to support analytics and model development. Applied SEO-driven data analysis, keyword optimization, and content intelligence techniques to enhance digital performance, and deployed containerized ML services with Docker while ensuring reliable model monitoring and inference performance.
Tools I use to move from idea to useful, testable implementation.
Python, Kotlin, Swift (Basic).
LLMs, RAG, LangChain, LangGraph, prompt engineering, Semantic search.
Scikit-learn, PyTorch, TensorFlow, Keras, OpenCV, CNNs, RNNs, model evaluation, feature engineering.
FastAPI, Flask, REST APIs, Node.js, microservices.
Docker, Kubernetes, CI/CD, Azure, AWS & GCP (Learning) .
MongoDB, Neo4j, Firebase.
Streamlit, Android development.
Git, GitHub.
Selected software work across repositories, AI tooling, and applied data projects.
ProRepo is an AI-powered repository and talent discovery platform that transforms student projects into intelligent innovation portfolios by analyzing uploaded documents, images, videos, and project artifacts to extract technical skills, domains, complexity, scalability, and innovation depth. The platform automatically classifies projects using NLP, tracks student growth across multiple domains, and uses an adaptive ranking engine to evaluate innovation, cross-domain expertise, and project impact beyond traditional grades and resumes. By connecting students with internships, industry challenges, mentors, funding opportunities, and recruiters through AI-driven matching, ProRepo creates a transparent and scalable ecosystem that bridges academia and industry while enabling resume-less talent discovery and continuous learning.
Public repositories across AI, data science, and experiments.
Formal learning paired with industry certificates in cloud and generative AI.
Focused on data science, machine learning, AI systems, and practical software development through academic work, projects, and applied technical exploration.
Built a strong foundation in analytical thinking, mathematics, and science while preparing for a deeper path into computing and technology.
Developed early interest in computer science, structured problem-solving, and disciplined learning through a broad academic foundation.
Hackathon outcomes and contributions to AI reliability, observability, and autonomous agent tooling.
Recognized for building and presenting an AI-focused solution in a competitive hackathon environment.
Selected as a finalist for SIH 2025, working on practical problem-solving through technology and team execution.
Contributed to an AI-powered Site Reliability Engineering platform by improving observability workflows, automation capabilities, issue analysis pipelines, and reliability-focused system components.
Contributed to transformer-based autonomous LLM agent systems with adaptive graph execution, node-level failure recovery, structured logging, and execution tracing.
Health Metrics and Activity Analysis Based on Gym Data, focused on using activity and health data to surface meaningful fitness insights.
I am a Pro Esports CODM Player, Someone who loves collecting die-casts cars
For roles, internships or Full-Time, reach me directly by email.